Overview

Dataset statistics

Number of variables33
Number of observations13815
Missing cells58818
Missing cells (%)12.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 MiB
Average record size in memory310.3 B

Variable types

Categorical11
Numeric19
Unsupported2
Boolean1

Alerts

name has a high cardinality: 13205 distinct valuesHigh cardinality
host_name has a high cardinality: 3792 distinct valuesHigh cardinality
last_review has a high cardinality: 1243 distinct valuesHigh cardinality
property_type has a high cardinality: 52 distinct valuesHigh cardinality
first_review has a high cardinality: 2708 distinct valuesHigh cardinality
listing_url has a high cardinality: 13815 distinct valuesHigh cardinality
host_about has a high cardinality: 5856 distinct valuesHigh cardinality
host_response_rate has a high cardinality: 63 distinct valuesHigh cardinality
longitude is highly overall correlated with neighbourhoodHigh correlation
price is highly overall correlated with accommodatesHigh correlation
number_of_reviews is highly overall correlated with number_of_reviews_ltmHigh correlation
reviews_per_month is highly overall correlated with number_of_reviews_ltmHigh correlation
calculated_host_listings_count is highly overall correlated with host_response_rateHigh correlation
number_of_reviews_ltm is highly overall correlated with number_of_reviews and 1 other fieldsHigh correlation
accommodates is highly overall correlated with priceHigh correlation
review_scores_value is highly overall correlated with review_scores_accuracy and 1 other fieldsHigh correlation
review_scores_cleanliness is highly overall correlated with review_scores_accuracy and 1 other fieldsHigh correlation
review_scores_accuracy is highly overall correlated with review_scores_value and 4 other fieldsHigh correlation
review_scores_communication is highly overall correlated with review_scores_accuracy and 2 other fieldsHigh correlation
review_scores_checkin is highly overall correlated with review_scores_accuracy and 1 other fieldsHigh correlation
review_scores_rating is highly overall correlated with review_scores_value and 3 other fieldsHigh correlation
neighbourhood is highly overall correlated with longitudeHigh correlation
room_type is highly overall correlated with property_typeHigh correlation
property_type is highly overall correlated with room_typeHigh correlation
host_response_time is highly overall correlated with host_response_rateHigh correlation
host_response_rate is highly overall correlated with calculated_host_listings_count and 1 other fieldsHigh correlation
room_type is highly imbalanced (73.3%)Imbalance
property_type is highly imbalanced (57.9%)Imbalance
host_is_superhost is highly imbalanced (50.4%)Imbalance
host_response_rate is highly imbalanced (69.1%)Imbalance
neighbourhood_group has 13815 (100.0%) missing valuesMissing
last_review has 1645 (11.9%) missing valuesMissing
reviews_per_month has 1645 (11.9%) missing valuesMissing
license has 13815 (100.0%) missing valuesMissing
first_review has 1645 (11.9%) missing valuesMissing
review_scores_value has 1688 (12.2%) missing valuesMissing
review_scores_cleanliness has 1688 (12.2%) missing valuesMissing
review_scores_location has 1689 (12.2%) missing valuesMissing
review_scores_accuracy has 1688 (12.2%) missing valuesMissing
review_scores_communication has 1688 (12.2%) missing valuesMissing
review_scores_checkin has 1688 (12.2%) missing valuesMissing
review_scores_rating has 1645 (11.9%) missing valuesMissing
host_about has 6970 (50.5%) missing valuesMissing
host_response_time has 3753 (27.2%) missing valuesMissing
host_response_rate has 3753 (27.2%) missing valuesMissing
price is highly skewed (γ1 = 22.74963003)Skewed
minimum_nights is highly skewed (γ1 = 33.10781304)Skewed
name is uniformly distributedUniform
listing_url is uniformly distributedUniform
listing_url has unique valuesUnique
neighbourhood_group is an unsupported type, check if it needs cleaning or further analysisUnsupported
license is an unsupported type, check if it needs cleaning or further analysisUnsupported
number_of_reviews has 1645 (11.9%) zerosZeros
availability_365 has 4846 (35.1%) zerosZeros
number_of_reviews_ltm has 3898 (28.2%) zerosZeros

Reproduction

Analysis started2023-01-21 14:07:26.145317
Analysis finished2023-01-21 14:08:34.197183
Duration1 minute and 8.05 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct13205
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size215.9 KiB
Cozy apartment in the heart of Copenhagen
 
21
Cozy apartment in the heart of Nørrebro
 
19
Skøn lejlighed i København
 
19
Charming apartment in the heart of Copenhagen
 
14
Cozy apartment in Copenhagen
 
14
Other values (13200)
13728 

Length

Max length127
Median length107
Mean length40.617879
Min length1

Characters and Unicode

Total characters561136
Distinct characters182
Distinct categories20 ?
Distinct scripts5 ?
Distinct blocks12 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12909 ?
Unique (%)93.4%

Sample

1st rowCopenhagen 'N Livin'
2nd rowLovely house - most attractive area
3rd rowCity Centre Townhouse Sleeps 1-10 persons
4th rowBest Location in Cool Istedgade
5th rowBeautiful, spacious, central, renovated Penthouse

Common Values

ValueCountFrequency (%)
Cozy apartment in the heart of Copenhagen 21
 
0.2%
Cozy apartment in the heart of Nørrebro 19
 
0.1%
Skøn lejlighed i København 19
 
0.1%
Charming apartment in the heart of Copenhagen 14
 
0.1%
Cozy apartment in Copenhagen 14
 
0.1%
Lovely apartment in the heart of Copenhagen 13
 
0.1%
Skøn lejlighed med gårdhave 13
 
0.1%
Spacious apartment in the heart of Copenhagen 12
 
0.1%
Skøn lejlighed på Frederiksberg 11
 
0.1%
Charming apartment in Copenhagen 10
 
0.1%
Other values (13195) 13669
98.9%

Length

2023-01-21T15:08:34.327346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
apartment 5215
 
5.8%
in 4746
 
5.3%
the 2433
 
2.7%
copenhagen 2301
 
2.6%
2230
 
2.5%
and 1820
 
2.0%
lejlighed 1767
 
2.0%
with 1734
 
1.9%
of 1567
 
1.8%
to 1454
 
1.6%
Other values (4414) 64136
71.7%

Most occurring characters

ValueCountFrequency (%)
75787
13.5%
e 55164
 
9.8%
t 39563
 
7.1%
a 37584
 
6.7%
r 36121
 
6.4%
n 36004
 
6.4%
o 31618
 
5.6%
i 26844
 
4.8%
l 20458
 
3.6%
h 17174
 
3.1%
Other values (172) 184819
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 435885
77.7%
Space Separator 75802
 
13.5%
Uppercase Letter 32876
 
5.9%
Other Punctuation 6520
 
1.2%
Decimal Number 6062
 
1.1%
Dash Punctuation 2538
 
0.5%
Close Punctuation 391
 
0.1%
Open Punctuation 344
 
0.1%
Other Symbol 268
 
< 0.1%
Math Symbol 248
 
< 0.1%
Other values (10) 202
 
< 0.1%

Most frequent character per category

Other Symbol
ValueCountFrequency (%)
181
67.5%
18
 
6.7%
12
 
4.5%
9
 
3.4%
4
 
1.5%
🌞 3
 
1.1%
3
 
1.1%
🌟 2
 
0.7%
🌻 2
 
0.7%
2
 
0.7%
Other values (29) 32
 
11.9%
Lowercase Letter
ValueCountFrequency (%)
e 55164
12.7%
t 39563
 
9.1%
a 37584
 
8.6%
r 36121
 
8.3%
n 36004
 
8.3%
o 31618
 
7.3%
i 26844
 
6.2%
l 20458
 
4.7%
h 17174
 
3.9%
m 14829
 
3.4%
Other values (27) 120526
27.7%
Uppercase Letter
ValueCountFrequency (%)
C 7788
23.7%
S 2525
 
7.7%
N 2435
 
7.4%
B 2103
 
6.4%
L 2077
 
6.3%
A 1895
 
5.8%
H 1812
 
5.5%
P 1629
 
5.0%
V 1325
 
4.0%
F 1278
 
3.9%
Other values (19) 8009
24.4%
Other Letter
ValueCountFrequency (%)
2
 
10.5%
2
 
10.5%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (7) 7
36.8%
Other Punctuation
ValueCountFrequency (%)
, 2428
37.2%
. 2028
31.1%
& 623
 
9.6%
! 544
 
8.3%
/ 421
 
6.5%
" 116
 
1.8%
' 108
 
1.7%
: 93
 
1.4%
· 72
 
1.1%
* 37
 
0.6%
Other values (6) 50
 
0.8%
Decimal Number
ValueCountFrequency (%)
2 1681
27.7%
1 1401
23.1%
0 797
13.1%
3 643
 
10.6%
5 484
 
8.0%
4 375
 
6.2%
6 206
 
3.4%
7 182
 
3.0%
8 162
 
2.7%
9 131
 
2.2%
Math Symbol
ValueCountFrequency (%)
| 130
52.4%
+ 97
39.1%
< 17
 
6.9%
~ 2
 
0.8%
= 1
 
0.4%
> 1
 
0.4%
Modifier Symbol
ValueCountFrequency (%)
´ 5
45.5%
^ 4
36.4%
` 1
 
9.1%
🏼 1
 
9.1%
Dash Punctuation
ValueCountFrequency (%)
- 2524
99.4%
12
 
0.5%
2
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 381
97.4%
] 9
 
2.3%
1
 
0.3%
Final Punctuation
ValueCountFrequency (%)
45
75.0%
14
 
23.3%
» 1
 
1.7%
Initial Punctuation
ValueCountFrequency (%)
14
66.7%
6
28.6%
« 1
 
4.8%
Format
ValueCountFrequency (%)
­ 2
50.0%
1
25.0%
1
25.0%
Space Separator
ValueCountFrequency (%)
75787
> 99.9%
  15
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 335
97.4%
[ 9
 
2.6%
Control
ValueCountFrequency (%)
45
100.0%
Nonspacing Mark
ValueCountFrequency (%)
33
100.0%
Other Number
ValueCountFrequency (%)
² 6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Currency Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 468761
83.5%
Common 92322
 
16.5%
Inherited 34
 
< 0.1%
Han 14
 
< 0.1%
Hiragana 5
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
75787
82.1%
- 2524
 
2.7%
, 2428
 
2.6%
. 2028
 
2.2%
2 1681
 
1.8%
1 1401
 
1.5%
0 797
 
0.9%
3 643
 
0.7%
& 623
 
0.7%
! 544
 
0.6%
Other values (87) 3866
 
4.2%
Latin
ValueCountFrequency (%)
e 55164
 
11.8%
t 39563
 
8.4%
a 37584
 
8.0%
r 36121
 
7.7%
n 36004
 
7.7%
o 31618
 
6.7%
i 26844
 
5.7%
l 20458
 
4.4%
h 17174
 
3.7%
m 14829
 
3.2%
Other values (56) 153402
32.7%
Han
ValueCountFrequency (%)
2
14.3%
2
14.3%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
Other values (2) 2
14.3%
Hiragana
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Inherited
ValueCountFrequency (%)
33
97.1%
1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 555066
98.9%
None 5672
 
1.0%
Misc Symbols 209
 
< 0.1%
Punctuation 100
 
< 0.1%
VS 33
 
< 0.1%
Dingbats 27
 
< 0.1%
CJK 14
 
< 0.1%
Emoticons 6
 
< 0.1%
Hiragana 5
 
< 0.1%
Enclosed Alphanum Sup 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
75787
13.7%
e 55164
 
9.9%
t 39563
 
7.1%
a 37584
 
6.8%
r 36121
 
6.5%
n 36004
 
6.5%
o 31618
 
5.7%
i 26844
 
4.8%
l 20458
 
3.7%
h 17174
 
3.1%
Other values (82) 178749
32.2%
None
ValueCountFrequency (%)
ø 2785
49.1%
å 1210
21.3%
æ 1084
 
19.1%
Ø 440
 
7.8%
· 72
 
1.3%
  15
 
0.3%
é 8
 
0.1%
² 6
 
0.1%
Å 5
 
0.1%
´ 5
 
0.1%
Other values (31) 42
 
0.7%
Misc Symbols
ValueCountFrequency (%)
181
86.6%
12
 
5.7%
9
 
4.3%
4
 
1.9%
1
 
0.5%
1
 
0.5%
1
 
0.5%
Punctuation
ValueCountFrequency (%)
45
45.0%
14
 
14.0%
14
 
14.0%
12
 
12.0%
6
 
6.0%
5
 
5.0%
2
 
2.0%
1
 
1.0%
1
 
1.0%
VS
ValueCountFrequency (%)
33
100.0%
Dingbats
ValueCountFrequency (%)
18
66.7%
3
 
11.1%
2
 
7.4%
2
 
7.4%
1
 
3.7%
1
 
3.7%
CJK
ValueCountFrequency (%)
2
14.3%
2
14.3%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
Other values (2) 2
14.3%
Emoticons
ValueCountFrequency (%)
😊 2
33.3%
😻 1
16.7%
🙂 1
16.7%
🙏 1
16.7%
😍 1
16.7%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🇰 1
50.0%
🇩 1
50.0%
Hiragana
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Enclosed Alphanum
ValueCountFrequency (%)
1
100.0%
Currency Symbols
ValueCountFrequency (%)
1
100.0%

host_id
Real number (ℝ)

Distinct12437
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.064145 × 108
Minimum11718
Maximum4.8058027 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:34.479856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum11718
5-th percentile2370627.4
Q113563826
median46563012
Q31.5534864 × 108
95-th percentile4.2793561 × 108
Maximum4.8058027 × 108
Range4.8056855 × 108
Interquartile range (IQR)1.4178481 × 108

Descriptive statistics

Standard deviation1.3028012 × 108
Coefficient of variation (CV)1.2242704
Kurtosis1.1312881
Mean1.064145 × 108
Median Absolute Deviation (MAD)40510540
Skewness1.4827853
Sum1.4701163 × 1012
Variance1.697291 × 1016
MonotonicityNot monotonic
2023-01-21T15:08:35.528168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
187610263 179
 
1.3%
155348640 82
 
0.6%
437864658 55
 
0.4%
424093643 26
 
0.2%
179753578 24
 
0.2%
461726397 17
 
0.1%
449069915 16
 
0.1%
3366007 13
 
0.1%
244829847 13
 
0.1%
2806924 12
 
0.1%
Other values (12427) 13378
96.8%
ValueCountFrequency (%)
11718 1
< 0.1%
12155 1
< 0.1%
16774 1
< 0.1%
18939 1
< 0.1%
18968 1
< 0.1%
21375 1
< 0.1%
33238 1
< 0.1%
33741 1
< 0.1%
45243 1
< 0.1%
49071 1
< 0.1%
ValueCountFrequency (%)
480580266 1
< 0.1%
480482332 1
< 0.1%
480322216 1
< 0.1%
480291576 1
< 0.1%
480193317 1
< 0.1%
480156533 1
< 0.1%
479904066 1
< 0.1%
479856647 1
< 0.1%
479715426 1
< 0.1%
479597843 1
< 0.1%

host_name
Categorical

Distinct3792
Distinct (%)27.5%
Missing1
Missing (%)< 0.1%
Memory size215.9 KiB
ApartmentinCopenhagen
 
179
Christian
 
145
Mette
 
144
Maria
 
125
Julie
 
120
Other values (3787)
13101 

Length

Max length31
Median length27
Mean length7.0167946
Min length1

Characters and Unicode

Total characters96930
Distinct characters99
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2749 ?
Unique (%)19.9%

Sample

1st rowSimon
2nd rowKari
3rd rowJulia
4th rowNana
5th rowEbbe

Common Values

ValueCountFrequency (%)
ApartmentinCopenhagen 179
 
1.3%
Christian 145
 
1.0%
Mette 144
 
1.0%
Maria 125
 
0.9%
Julie 120
 
0.9%
Anna 113
 
0.8%
Rasmus 112
 
0.8%
Mads 106
 
0.8%
Thomas 106
 
0.8%
Louise 104
 
0.8%
Other values (3782) 12560
90.9%

Length

2023-01-21T15:08:35.729373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maria 181
 
1.1%
apartmentincopenhagen 179
 
1.1%
christian 179
 
1.1%
mette 178
 
1.1%
anne 172
 
1.1%
170
 
1.1%
julie 149
 
0.9%
marie 149
 
0.9%
anna 140
 
0.9%
sofie 137
 
0.8%
Other values (3226) 14527
89.9%

Most occurring characters

ValueCountFrequency (%)
e 11246
 
11.6%
a 10928
 
11.3%
i 8898
 
9.2%
n 8626
 
8.9%
r 6324
 
6.5%
t 4500
 
4.6%
l 4316
 
4.5%
s 4129
 
4.3%
o 3534
 
3.6%
2354
 
2.4%
Other values (89) 32075
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 77880
80.3%
Uppercase Letter 16326
 
16.8%
Space Separator 2354
 
2.4%
Other Punctuation 211
 
0.2%
Dash Punctuation 122
 
0.1%
Close Punctuation 10
 
< 0.1%
Open Punctuation 10
 
< 0.1%
Decimal Number 10
 
< 0.1%
Math Symbol 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11246
14.4%
a 10928
14.0%
i 8898
11.4%
n 8626
11.1%
r 6324
8.1%
t 4500
 
5.8%
l 4316
 
5.5%
s 4129
 
5.3%
o 3534
 
4.5%
h 2270
 
2.9%
Other values (43) 13109
16.8%
Uppercase Letter
ValueCountFrequency (%)
M 2117
13.0%
A 1783
10.9%
S 1640
 
10.0%
C 1372
 
8.4%
J 1279
 
7.8%
L 1069
 
6.5%
K 893
 
5.5%
T 735
 
4.5%
N 621
 
3.8%
R 588
 
3.6%
Other values (22) 4229
25.9%
Decimal Number
ValueCountFrequency (%)
5 6
60.0%
3 1
 
10.0%
6 1
 
10.0%
1 1
 
10.0%
7 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
& 158
74.9%
. 47
 
22.3%
/ 3
 
1.4%
, 3
 
1.4%
Space Separator
ValueCountFrequency (%)
2354
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 122
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Math Symbol
ValueCountFrequency (%)
+ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 94206
97.2%
Common 2724
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11246
 
11.9%
a 10928
 
11.6%
i 8898
 
9.4%
n 8626
 
9.2%
r 6324
 
6.7%
t 4500
 
4.8%
l 4316
 
4.6%
s 4129
 
4.4%
o 3534
 
3.8%
h 2270
 
2.4%
Other values (75) 29435
31.2%
Common
ValueCountFrequency (%)
2354
86.4%
& 158
 
5.8%
- 122
 
4.5%
. 47
 
1.7%
) 10
 
0.4%
( 10
 
0.4%
+ 7
 
0.3%
5 6
 
0.2%
/ 3
 
0.1%
, 3
 
0.1%
Other values (4) 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96555
99.6%
None 375
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11246
 
11.6%
a 10928
 
11.3%
i 8898
 
9.2%
n 8626
 
8.9%
r 6324
 
6.5%
t 4500
 
4.7%
l 4316
 
4.5%
s 4129
 
4.3%
o 3534
 
3.7%
2354
 
2.4%
Other values (56) 31700
32.8%
None
ValueCountFrequency (%)
ø 205
54.7%
æ 52
 
13.9%
é 30
 
8.0%
Ø 14
 
3.7%
å 10
 
2.7%
ö 9
 
2.4%
ó 7
 
1.9%
ü 7
 
1.9%
ð 5
 
1.3%
í 5
 
1.3%
Other values (23) 31
 
8.3%

neighbourhood_group
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing13815
Missing (%)100.0%
Memory size215.9 KiB

neighbourhood
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size215.9 KiB
Vesterbro-Kongens Enghave
2290 
Nrrebro
2268 
Indre By
2068 
sterbro
1511 
Frederiksberg
1367 
Other values (6)
4311 

Length

Max length25
Median length12
Mean length11.386174
Min length5

Characters and Unicode

Total characters157300
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNrrebro
2nd rowIndre By
3rd rowIndre By
4th rowVesterbro-Kongens Enghave
5th rowVesterbro-Kongens Enghave

Common Values

ValueCountFrequency (%)
Vesterbro-Kongens Enghave 2290
16.6%
Nrrebro 2268
16.4%
Indre By 2068
15.0%
sterbro 1511
10.9%
Frederiksberg 1367
9.9%
Amager Vest 1235
8.9%
Amager st 1026
7.4%
Bispebjerg 698
 
5.1%
Valby 663
 
4.8%
Vanlse 350
 
2.5%

Length

2023-01-21T15:08:35.898249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vesterbro-kongens 2290
11.2%
enghave 2290
11.2%
nrrebro 2268
11.1%
amager 2261
11.1%
indre 2068
10.1%
by 2068
10.1%
sterbro 1511
7.4%
frederiksberg 1367
6.7%
vest 1235
6.0%
st 1026
5.0%
Other values (4) 2050
10.0%

Most occurring characters

ValueCountFrequency (%)
e 24350
15.5%
r 23873
15.2%
s 11445
 
7.3%
n 9627
 
6.1%
g 8906
 
5.7%
b 8797
 
5.6%
o 8359
 
5.3%
6619
 
4.2%
t 6062
 
3.9%
a 5564
 
3.5%
Other values (21) 43698
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 127526
81.1%
Uppercase Letter 20526
 
13.0%
Space Separator 6619
 
4.2%
Dash Punctuation 2629
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 24350
19.1%
r 23873
18.7%
s 11445
9.0%
n 9627
 
7.5%
g 8906
 
7.0%
b 8797
 
6.9%
o 8359
 
6.6%
t 6062
 
4.8%
a 5564
 
4.4%
d 3435
 
2.7%
Other values (10) 17108
13.4%
Uppercase Letter
ValueCountFrequency (%)
V 4538
22.1%
B 3105
15.1%
E 2290
11.2%
K 2290
11.2%
N 2268
11.0%
A 2261
11.0%
I 2068
10.1%
F 1367
 
6.7%
H 339
 
1.7%
Space Separator
ValueCountFrequency (%)
6619
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2629
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 148052
94.1%
Common 9248
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 24350
16.4%
r 23873
16.1%
s 11445
 
7.7%
n 9627
 
6.5%
g 8906
 
6.0%
b 8797
 
5.9%
o 8359
 
5.6%
t 6062
 
4.1%
a 5564
 
3.8%
V 4538
 
3.1%
Other values (19) 36531
24.7%
Common
ValueCountFrequency (%)
6619
71.6%
- 2629
 
28.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 157300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 24350
15.5%
r 23873
15.2%
s 11445
 
7.3%
n 9627
 
6.1%
g 8906
 
5.7%
b 8797
 
5.6%
o 8359
 
5.3%
6619
 
4.2%
t 6062
 
3.9%
a 5564
 
3.5%
Other values (21) 43698
27.8%

latitude
Real number (ℝ)

Distinct8432
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.680569
Minimum55.60951
Maximum55.7428
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:36.058592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum55.60951
5-th percentile55.646247
Q155.66611
median55.68119
Q355.69577
95-th percentile55.711329
Maximum55.7428
Range0.13329
Interquartile range (IQR)0.02966

Descriptive statistics

Standard deviation0.021088474
Coefficient of variation (CV)0.00037874027
Kurtosis0.12564487
Mean55.680569
Median Absolute Deviation (MAD)0.0149507
Skewness-0.066060023
Sum769227.06
Variance0.00044472372
MonotonicityNot monotonic
2023-01-21T15:08:36.259531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.66658 9
 
0.1%
55.66538 9
 
0.1%
55.66005 9
 
0.1%
55.66926 8
 
0.1%
55.67023 8
 
0.1%
55.66714 8
 
0.1%
55.66583 8
 
0.1%
55.67714 8
 
0.1%
55.66619 7
 
0.1%
55.69999 7
 
0.1%
Other values (8422) 13734
99.4%
ValueCountFrequency (%)
55.60951 1
< 0.1%
55.61186 1
< 0.1%
55.6121384 1
< 0.1%
55.613216 1
< 0.1%
55.61336 1
< 0.1%
55.61343 1
< 0.1%
55.61356071 1
< 0.1%
55.61499 1
< 0.1%
55.61519 1
< 0.1%
55.615391 1
< 0.1%
ValueCountFrequency (%)
55.7428 1
< 0.1%
55.74273 1
< 0.1%
55.7427 1
< 0.1%
55.74267 1
< 0.1%
55.74265 1
< 0.1%
55.74258 1
< 0.1%
55.74249 1
< 0.1%
55.74246 1
< 0.1%
55.74243 1
< 0.1%
55.74241 1
< 0.1%

longitude
Real number (ℝ)

Distinct9663
Distinct (%)69.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.557805
Minimum12.43567
Maximum12.65174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:36.403893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12.43567
5-th percentile12.494894
Q112.540285
median12.55552
Q312.580741
95-th percentile12.614333
Maximum12.65174
Range0.21607
Interquartile range (IQR)0.040455522

Descriptive statistics

Standard deviation0.033511545
Coefficient of variation (CV)0.0026685829
Kurtosis0.40370677
Mean12.557805
Median Absolute Deviation (MAD)0.020459
Skewness-0.29914072
Sum173486.08
Variance0.0011230236
MonotonicityNot monotonic
2023-01-21T15:08:36.540027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.55347 9
 
0.1%
12.54652 8
 
0.1%
12.54526 7
 
0.1%
12.54301 7
 
0.1%
12.54948 7
 
0.1%
12.57523 7
 
0.1%
12.55351 6
 
< 0.1%
12.55167 6
 
< 0.1%
12.5466 6
 
< 0.1%
12.54637 6
 
< 0.1%
Other values (9653) 13746
99.5%
ValueCountFrequency (%)
12.43567 1
< 0.1%
12.43769 1
< 0.1%
12.43788 1
< 0.1%
12.43817 1
< 0.1%
12.43831 1
< 0.1%
12.43909 1
< 0.1%
12.43911 1
< 0.1%
12.43952 1
< 0.1%
12.44034 1
< 0.1%
12.44062 1
< 0.1%
ValueCountFrequency (%)
12.65174 1
< 0.1%
12.6491 1
< 0.1%
12.64645 1
< 0.1%
12.64593 1
< 0.1%
12.64571 1
< 0.1%
12.64545 1
< 0.1%
12.64498 1
< 0.1%
12.64495 1
< 0.1%
12.6446 1
< 0.1%
12.64399 1
< 0.1%

room_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size215.9 KiB
Entire home/apt
12230 
Private room
1551 
Shared room
 
19
Hotel room
 
15

Length

Max length15
Median length15
Mean length14.652262
Min length10

Characters and Unicode

Total characters202421
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntire home/apt
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 12230
88.5%
Private room 1551
 
11.2%
Shared room 19
 
0.1%
Hotel room 15
 
0.1%

Length

2023-01-21T15:08:36.773080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T15:08:36.940672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
entire 12230
44.3%
home/apt 12230
44.3%
room 1585
 
5.7%
private 1551
 
5.6%
shared 19
 
0.1%
hotel 15
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 26045
12.9%
t 26026
12.9%
o 15415
 
7.6%
r 15385
 
7.6%
m 13815
 
6.8%
13815
 
6.8%
a 13800
 
6.8%
i 13781
 
6.8%
h 12249
 
6.1%
p 12230
 
6.0%
Other values (9) 39860
19.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 162561
80.3%
Space Separator 13815
 
6.8%
Uppercase Letter 13815
 
6.8%
Other Punctuation 12230
 
6.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 26045
16.0%
t 26026
16.0%
o 15415
9.5%
r 15385
9.5%
m 13815
8.5%
a 13800
8.5%
i 13781
8.5%
h 12249
7.5%
p 12230
7.5%
n 12230
7.5%
Other values (3) 1585
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
E 12230
88.5%
P 1551
 
11.2%
S 19
 
0.1%
H 15
 
0.1%
Space Separator
ValueCountFrequency (%)
13815
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 12230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 176376
87.1%
Common 26045
 
12.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 26045
14.8%
t 26026
14.8%
o 15415
8.7%
r 15385
8.7%
m 13815
7.8%
a 13800
7.8%
i 13781
7.8%
h 12249
6.9%
p 12230
6.9%
E 12230
6.9%
Other values (7) 15400
8.7%
Common
ValueCountFrequency (%)
13815
53.0%
/ 12230
47.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 202421
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 26045
12.9%
t 26026
12.9%
o 15415
 
7.6%
r 15385
 
7.6%
m 13815
 
6.8%
13815
 
6.8%
a 13800
 
6.8%
i 13781
 
6.8%
h 12249
 
6.1%
p 12230
 
6.0%
Other values (9) 39860
19.7%

price
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1689
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1205.879
Minimum0
Maximum64900
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:37.060792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile400.7
Q1729
median995
Q31368.5
95-th percentile2483.6
Maximum64900
Range64900
Interquartile range (IQR)639.5

Descriptive statistics

Standard deviation1433.1431
Coefficient of variation (CV)1.1884635
Kurtosis835.22293
Mean1205.879
Median Absolute Deviation (MAD)295
Skewness22.74963
Sum16659218
Variance2053899.3
MonotonicityNot monotonic
2023-01-21T15:08:37.189381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 656
 
4.7%
1200 563
 
4.1%
800 506
 
3.7%
900 439
 
3.2%
1500 425
 
3.1%
700 364
 
2.6%
750 360
 
2.6%
1100 320
 
2.3%
850 312
 
2.3%
950 284
 
2.1%
Other values (1679) 9586
69.4%
ValueCountFrequency (%)
0 2
< 0.1%
79 1
< 0.1%
113 1
< 0.1%
120 1
< 0.1%
124 1
< 0.1%
127 1
< 0.1%
134 1
< 0.1%
135 1
< 0.1%
143 1
< 0.1%
145 1
< 0.1%
ValueCountFrequency (%)
64900 1
< 0.1%
62000 1
< 0.1%
58000 1
< 0.1%
44000 1
< 0.1%
41800 1
< 0.1%
36200 1
< 0.1%
20000 2
< 0.1%
17857 1
< 0.1%
15000 1
< 0.1%
14000 1
< 0.1%

minimum_nights
Real number (ℝ)

Distinct69
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6055737
Minimum1
Maximum1111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:37.373932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile8
Maximum1111
Range1110
Interquartile range (IQR)2

Descriptive statistics

Standard deviation16.900153
Coefficient of variation (CV)3.6695
Kurtosis1650.7366
Mean4.6055737
Median Absolute Deviation (MAD)1
Skewness33.107813
Sum63626
Variance285.61516
MonotonicityNot monotonic
2023-01-21T15:08:37.511268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3641
26.4%
3 3140
22.7%
1 2283
16.5%
4 1698
12.3%
5 1352
 
9.8%
7 550
 
4.0%
6 436
 
3.2%
14 149
 
1.1%
10 109
 
0.8%
30 107
 
0.8%
Other values (59) 350
 
2.5%
ValueCountFrequency (%)
1 2283
16.5%
2 3641
26.4%
3 3140
22.7%
4 1698
12.3%
5 1352
 
9.8%
6 436
 
3.2%
7 550
 
4.0%
8 30
 
0.2%
9 13
 
0.1%
10 109
 
0.8%
ValueCountFrequency (%)
1111 1
 
< 0.1%
600 1
 
< 0.1%
500 2
< 0.1%
400 1
 
< 0.1%
365 1
 
< 0.1%
360 2
< 0.1%
350 1
 
< 0.1%
300 2
< 0.1%
210 1
 
< 0.1%
200 3
< 0.1%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct266
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.571118
Minimum0
Maximum711
Zeros1645
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:37.664358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q320
95-th percentile73
Maximum711
Range711
Interquartile range (IQR)18

Descriptive statistics

Standard deviation36.402671
Coefficient of variation (CV)1.9601766
Kurtosis62.672983
Mean18.571118
Median Absolute Deviation (MAD)6
Skewness6.279766
Sum256560
Variance1325.1545
MonotonicityNot monotonic
2023-01-21T15:08:37.859837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1645
 
11.9%
1 1067
 
7.7%
2 1008
 
7.3%
3 920
 
6.7%
4 749
 
5.4%
5 640
 
4.6%
6 584
 
4.2%
8 456
 
3.3%
7 449
 
3.3%
9 373
 
2.7%
Other values (256) 5924
42.9%
ValueCountFrequency (%)
0 1645
11.9%
1 1067
7.7%
2 1008
7.3%
3 920
6.7%
4 749
5.4%
5 640
 
4.6%
6 584
 
4.2%
7 449
 
3.3%
8 456
 
3.3%
9 373
 
2.7%
ValueCountFrequency (%)
711 1
< 0.1%
602 1
< 0.1%
599 1
< 0.1%
550 1
< 0.1%
536 1
< 0.1%
533 1
< 0.1%
520 1
< 0.1%
494 1
< 0.1%
486 1
< 0.1%
454 1
< 0.1%

last_review
Categorical

HIGH CARDINALITY  MISSING 

Distinct1243
Distinct (%)10.2%
Missing1645
Missing (%)11.9%
Memory size215.9 KiB
2022-09-18
 
409
2022-09-19
 
284
2022-09-11
 
259
2022-08-28
 
253
2022-09-04
 
239
Other values (1238)
10726 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters121700
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique526 ?
Unique (%)4.3%

Sample

1st row2022-06-21
2nd row2022-08-09
3rd row2022-09-10
4th row2022-08-04
5th row2022-08-22

Common Values

ValueCountFrequency (%)
2022-09-18 409
 
3.0%
2022-09-19 284
 
2.1%
2022-09-11 259
 
1.9%
2022-08-28 253
 
1.8%
2022-09-04 239
 
1.7%
2022-08-21 220
 
1.6%
2022-08-07 211
 
1.5%
2022-08-29 189
 
1.4%
2022-07-31 185
 
1.3%
2022-08-14 158
 
1.1%
Other values (1233) 9763
70.7%
(Missing) 1645
 
11.9%

Length

2023-01-21T15:08:37.990554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-09-18 409
 
3.4%
2022-09-19 284
 
2.3%
2022-09-11 259
 
2.1%
2022-08-28 253
 
2.1%
2022-09-04 239
 
2.0%
2022-08-21 220
 
1.8%
2022-08-07 211
 
1.7%
2022-08-29 189
 
1.6%
2022-07-31 185
 
1.5%
2022-08-14 158
 
1.3%
Other values (1233) 9763
80.2%

Most occurring characters

ValueCountFrequency (%)
2 38181
31.4%
0 28843
23.7%
- 24340
20.0%
1 9001
 
7.4%
8 5727
 
4.7%
9 5510
 
4.5%
7 3545
 
2.9%
3 2006
 
1.6%
6 1716
 
1.4%
5 1418
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 97360
80.0%
Dash Punctuation 24340
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 38181
39.2%
0 28843
29.6%
1 9001
 
9.2%
8 5727
 
5.9%
9 5510
 
5.7%
7 3545
 
3.6%
3 2006
 
2.1%
6 1716
 
1.8%
5 1418
 
1.5%
4 1413
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 24340
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 121700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 38181
31.4%
0 28843
23.7%
- 24340
20.0%
1 9001
 
7.4%
8 5727
 
4.7%
9 5510
 
4.5%
7 3545
 
2.9%
3 2006
 
1.6%
6 1716
 
1.4%
5 1418
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 38181
31.4%
0 28843
23.7%
- 24340
20.0%
1 9001
 
7.4%
8 5727
 
4.7%
9 5510
 
4.5%
7 3545
 
2.9%
3 2006
 
1.6%
6 1716
 
1.4%
5 1418
 
1.2%

reviews_per_month
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct562
Distinct (%)4.6%
Missing1645
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean0.90381594
Minimum0.01
Maximum24.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:38.171674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.07
Q10.23
median0.53
Q31.12
95-th percentile3
Maximum24.02
Range24.01
Interquartile range (IQR)0.89

Descriptive statistics

Standard deviation1.1107542
Coefficient of variation (CV)1.2289607
Kurtosis33.81649
Mean0.90381594
Median Absolute Deviation (MAD)0.37
Skewness3.8100193
Sum10999.44
Variance1.233775
MonotonicityNot monotonic
2023-01-21T15:08:38.489921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 225
 
1.6%
0.08 204
 
1.5%
0.14 168
 
1.2%
0.18 160
 
1.2%
0.27 159
 
1.2%
0.22 157
 
1.1%
0.15 157
 
1.1%
0.13 154
 
1.1%
0.21 153
 
1.1%
0.19 151
 
1.1%
Other values (552) 10482
75.9%
(Missing) 1645
 
11.9%
ValueCountFrequency (%)
0.01 42
 
0.3%
0.02 71
 
0.5%
0.03 132
1.0%
0.04 98
0.7%
0.05 122
0.9%
0.06 118
0.9%
0.07 139
1.0%
0.08 204
1.5%
0.09 120
0.9%
0.1 147
1.1%
ValueCountFrequency (%)
24.02 1
< 0.1%
19.23 1
< 0.1%
15.74 1
< 0.1%
11.34 1
< 0.1%
10.9 1
< 0.1%
10.68 1
< 0.1%
10.48 1
< 0.1%
10.03 1
< 0.1%
9.99 1
< 0.1%
9.95 1
< 0.1%
Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4006515
Minimum1
Maximum179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:38.602808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile4
Maximum179
Range178
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.298565
Coefficient of variation (CV)4.8398664
Kurtosis56.722032
Mean4.4006515
Median Absolute Deviation (MAD)0
Skewness7.4805991
Sum60795
Variance453.62888
MonotonicityNot monotonic
2023-01-21T15:08:38.707153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 11763
85.1%
2 1068
 
7.7%
3 234
 
1.7%
179 179
 
1.3%
82 82
 
0.6%
4 68
 
0.5%
55 55
 
0.4%
5 50
 
0.4%
7 49
 
0.4%
6 48
 
0.3%
Other values (10) 219
 
1.6%
ValueCountFrequency (%)
1 11763
85.1%
2 1068
 
7.7%
3 234
 
1.7%
4 68
 
0.5%
5 50
 
0.4%
6 48
 
0.3%
7 49
 
0.4%
8 16
 
0.1%
9 9
 
0.1%
10 40
 
0.3%
ValueCountFrequency (%)
179 179
1.3%
82 82
0.6%
55 55
 
0.4%
26 26
 
0.2%
24 24
 
0.2%
17 17
 
0.1%
16 16
 
0.1%
13 26
 
0.2%
12 12
 
0.1%
11 33
 
0.2%

availability_365
Real number (ℝ)

Distinct366
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.302642
Minimum0
Maximum365
Zeros4846
Zeros (%)35.1%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:38.836034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q3174
95-th percentile353
Maximum365
Range365
Interquartile range (IQR)174

Descriptive statistics

Standard deviation123.16196
Coefficient of variation (CV)1.3200265
Kurtosis-0.41460319
Mean93.302642
Median Absolute Deviation (MAD)20
Skewness1.060592
Sum1288976
Variance15168.868
MonotonicityNot monotonic
2023-01-21T15:08:38.956317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4846
35.1%
1 265
 
1.9%
365 180
 
1.3%
2 178
 
1.3%
8 160
 
1.2%
4 159
 
1.2%
3 158
 
1.1%
266 151
 
1.1%
7 150
 
1.1%
6 136
 
1.0%
Other values (356) 7432
53.8%
ValueCountFrequency (%)
0 4846
35.1%
1 265
 
1.9%
2 178
 
1.3%
3 158
 
1.1%
4 159
 
1.2%
5 130
 
0.9%
6 136
 
1.0%
7 150
 
1.1%
8 160
 
1.2%
9 131
 
0.9%
ValueCountFrequency (%)
365 180
1.3%
364 90
0.7%
363 53
 
0.4%
362 53
 
0.4%
361 20
 
0.1%
360 29
 
0.2%
359 45
 
0.3%
358 96
0.7%
357 37
 
0.3%
356 31
 
0.2%

number_of_reviews_ltm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4511039
Minimum0
Maximum471
Zeros3898
Zeros (%)28.2%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:39.094108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q36
95-th percentile19
Maximum471
Range471
Interquartile range (IQR)6

Descriptive statistics

Standard deviation11.373977
Coefficient of variation (CV)2.0865457
Kurtosis385.20437
Mean5.4511039
Median Absolute Deviation (MAD)3
Skewness13.410917
Sum75307
Variance129.36736
MonotonicityNot monotonic
2023-01-21T15:08:39.222136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3898
28.2%
1 1523
 
11.0%
2 1395
 
10.1%
3 1220
 
8.8%
4 969
 
7.0%
5 768
 
5.6%
6 672
 
4.9%
7 492
 
3.6%
8 394
 
2.9%
9 331
 
2.4%
Other values (91) 2153
15.6%
ValueCountFrequency (%)
0 3898
28.2%
1 1523
 
11.0%
2 1395
 
10.1%
3 1220
 
8.8%
4 969
 
7.0%
5 768
 
5.6%
6 672
 
4.9%
7 492
 
3.6%
8 394
 
2.9%
9 331
 
2.4%
ValueCountFrequency (%)
471 1
< 0.1%
411 1
< 0.1%
275 1
< 0.1%
216 1
< 0.1%
198 1
< 0.1%
197 1
< 0.1%
182 1
< 0.1%
154 1
< 0.1%
150 1
< 0.1%
144 2
< 0.1%

license
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing13815
Missing (%)100.0%
Memory size215.9 KiB

property_type
Categorical

HIGH CARDINALITY  HIGH CORRELATION  IMBALANCE 

Distinct52
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size215.9 KiB
Entire rental unit
6067 
Entire condo
4329 
Private room in rental unit
840 
Entire home
656 
Private room in condo
 
348
Other values (47)
1575 

Length

Max length34
Median length33
Mean length16.45067
Min length3

Characters and Unicode

Total characters227266
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st rowEntire rental unit
2nd rowEntire home
3rd rowEntire townhouse
4th rowEntire rental unit
5th rowEntire condo

Common Values

ValueCountFrequency (%)
Entire rental unit 6067
43.9%
Entire condo 4329
31.3%
Private room in rental unit 840
 
6.1%
Entire home 656
 
4.7%
Private room in condo 348
 
2.5%
Entire townhouse 346
 
2.5%
Entire serviced apartment 344
 
2.5%
Entire villa 230
 
1.7%
Private room in home 138
 
1.0%
Entire loft 135
 
1.0%
Other values (42) 382
 
2.8%

Length

2023-01-21T15:08:39.359431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
entire 12187
32.0%
unit 6913
18.1%
rental 6913
18.1%
condo 4679
 
12.3%
room 1586
 
4.2%
in 1584
 
4.2%
private 1519
 
4.0%
home 819
 
2.1%
townhouse 384
 
1.0%
apartment 358
 
0.9%
Other values (28) 1161
 
3.0%

Most occurring characters

ValueCountFrequency (%)
n 33089
14.6%
t 29047
12.8%
24288
10.7%
e 23210
10.2%
r 22952
10.1%
i 22926
10.1%
o 14442
6.4%
E 12187
 
5.4%
a 9654
 
4.2%
l 7702
 
3.4%
Other values (25) 27769
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 189148
83.2%
Space Separator 24288
 
10.7%
Uppercase Letter 13825
 
6.1%
Other Punctuation 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 33089
17.5%
t 29047
15.4%
e 23210
12.3%
r 22952
12.1%
i 22926
12.1%
o 14442
7.6%
a 9654
 
5.1%
l 7702
 
4.1%
u 7521
 
4.0%
d 5098
 
2.7%
Other values (13) 13507
7.1%
Uppercase Letter
ValueCountFrequency (%)
E 12187
88.2%
P 1519
 
11.0%
R 53
 
0.4%
S 19
 
0.1%
H 14
 
0.1%
T 13
 
0.1%
B 9
 
0.1%
C 5
 
< 0.1%
V 5
 
< 0.1%
M 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
24288
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 202973
89.3%
Common 24293
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 33089
16.3%
t 29047
14.3%
e 23210
11.4%
r 22952
11.3%
i 22926
11.3%
o 14442
7.1%
E 12187
 
6.0%
a 9654
 
4.8%
l 7702
 
3.8%
u 7521
 
3.7%
Other values (23) 20243
10.0%
Common
ValueCountFrequency (%)
24288
> 99.9%
/ 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 227266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 33089
14.6%
t 29047
12.8%
24288
10.7%
e 23210
10.2%
r 22952
10.1%
i 22926
10.1%
o 14442
6.4%
E 12187
 
5.4%
a 9654
 
4.2%
l 7702
 
3.4%
Other values (25) 27769
12.2%

accommodates
Real number (ℝ)

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4003619
Minimum0
Maximum16
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:39.464201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7283396
Coefficient of variation (CV)0.50828108
Kurtosis2.7733747
Mean3.4003619
Median Absolute Deviation (MAD)1
Skewness1.3250253
Sum46976
Variance2.9871578
MonotonicityNot monotonic
2023-01-21T15:08:39.552508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 5559
40.2%
4 3240
23.5%
3 1677
 
12.1%
6 1180
 
8.5%
5 1072
 
7.8%
1 487
 
3.5%
8 268
 
1.9%
7 184
 
1.3%
10 70
 
0.5%
9 39
 
0.3%
Other values (6) 39
 
0.3%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 487
 
3.5%
2 5559
40.2%
3 1677
 
12.1%
4 3240
23.5%
5 1072
 
7.8%
6 1180
 
8.5%
7 184
 
1.3%
8 268
 
1.9%
9 39
 
0.3%
ValueCountFrequency (%)
16 4
 
< 0.1%
15 2
 
< 0.1%
14 1
 
< 0.1%
12 19
 
0.1%
11 11
 
0.1%
10 70
 
0.5%
9 39
 
0.3%
8 268
 
1.9%
7 184
 
1.3%
6 1180
8.5%

first_review
Categorical

HIGH CARDINALITY  MISSING 

Distinct2708
Distinct (%)22.3%
Missing1645
Missing (%)11.9%
Memory size215.9 KiB
2022-08-07
 
63
2022-07-03
 
62
2022-05-29
 
58
2022-08-06
 
54
2022-04-17
 
51
Other values (2703)
11882 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters121700
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique844 ?
Unique (%)6.9%

Sample

1st row2009-09-04
2nd row2013-12-02
3rd row2010-10-14
4th row2010-06-17
5th row2010-08-16

Common Values

ValueCountFrequency (%)
2022-08-07 63
 
0.5%
2022-07-03 62
 
0.4%
2022-05-29 58
 
0.4%
2022-08-06 54
 
0.4%
2022-04-17 51
 
0.4%
2022-07-24 50
 
0.4%
2022-06-06 50
 
0.4%
2022-06-19 48
 
0.3%
2022-04-18 47
 
0.3%
2022-08-08 47
 
0.3%
Other values (2698) 11640
84.3%
(Missing) 1645
 
11.9%

Length

2023-01-21T15:08:39.688613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-08-07 63
 
0.5%
2022-07-03 62
 
0.5%
2022-05-29 58
 
0.5%
2022-08-06 54
 
0.4%
2022-04-17 51
 
0.4%
2022-07-24 50
 
0.4%
2022-06-06 50
 
0.4%
2022-06-19 48
 
0.4%
2022-04-18 47
 
0.4%
2022-08-08 47
 
0.4%
Other values (2698) 11640
95.6%

Most occurring characters

ValueCountFrequency (%)
0 29007
23.8%
2 27784
22.8%
- 24340
20.0%
1 15900
13.1%
7 4868
 
4.0%
8 4642
 
3.8%
9 3654
 
3.0%
6 3500
 
2.9%
5 2938
 
2.4%
3 2573
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 97360
80.0%
Dash Punctuation 24340
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29007
29.8%
2 27784
28.5%
1 15900
16.3%
7 4868
 
5.0%
8 4642
 
4.8%
9 3654
 
3.8%
6 3500
 
3.6%
5 2938
 
3.0%
3 2573
 
2.6%
4 2494
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 24340
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 121700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29007
23.8%
2 27784
22.8%
- 24340
20.0%
1 15900
13.1%
7 4868
 
4.0%
8 4642
 
3.8%
9 3654
 
3.0%
6 3500
 
2.9%
5 2938
 
2.4%
3 2573
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29007
23.8%
2 27784
22.8%
- 24340
20.0%
1 15900
13.1%
7 4868
 
4.0%
8 4642
 
3.8%
9 3654
 
3.0%
6 3500
 
2.9%
5 2938
 
2.4%
3 2573
 
2.1%

review_scores_value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct128
Distinct (%)1.1%
Missing1688
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean4.7203134
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:39.800968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.14
Q14.61
median4.78
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.39

Descriptive statistics

Standard deviation0.30828518
Coefficient of variation (CV)0.06531032
Kurtosis18.598551
Mean4.7203134
Median Absolute Deviation (MAD)0.18
Skewness-2.934072
Sum57243.24
Variance0.09503975
MonotonicityNot monotonic
2023-01-21T15:08:39.933512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 3037
22.0%
4.67 639
 
4.6%
4.5 622
 
4.5%
4.75 507
 
3.7%
4.8 397
 
2.9%
4 377
 
2.7%
4.83 333
 
2.4%
4.86 271
 
2.0%
4.88 258
 
1.9%
4.71 236
 
1.7%
Other values (118) 5450
39.4%
(Missing) 1688
 
12.2%
ValueCountFrequency (%)
1 6
 
< 0.1%
2 5
 
< 0.1%
2.33 1
 
< 0.1%
2.4 1
 
< 0.1%
2.5 3
 
< 0.1%
2.67 1
 
< 0.1%
3 49
0.4%
3.18 1
 
< 0.1%
3.2 2
 
< 0.1%
3.23 1
 
< 0.1%
ValueCountFrequency (%)
5 3037
22.0%
4.98 2
 
< 0.1%
4.97 13
 
0.1%
4.96 24
 
0.2%
4.95 43
 
0.3%
4.94 69
 
0.5%
4.93 100
 
0.7%
4.92 143
 
1.0%
4.91 100
 
0.7%
4.9 133
 
1.0%

review_scores_cleanliness
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct171
Distinct (%)1.4%
Missing1688
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean4.692313
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:40.156534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14.56
median4.8
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.3942319
Coefficient of variation (CV)0.084016538
Kurtosis14.23205
Mean4.692313
Median Absolute Deviation (MAD)0.2
Skewness-2.7881466
Sum56903.68
Variance0.15541879
MonotonicityNot monotonic
2023-01-21T15:08:40.325337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 3479
25.2%
4.5 546
 
4.0%
4.67 476
 
3.4%
4 447
 
3.2%
4.75 388
 
2.8%
4.8 286
 
2.1%
4.83 283
 
2.0%
4.88 248
 
1.8%
4.86 216
 
1.6%
4.33 210
 
1.5%
Other values (161) 5548
40.2%
(Missing) 1688
 
12.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 11
0.1%
1.67 1
 
< 0.1%
1.88 1
 
< 0.1%
2 13
0.1%
2.33 2
 
< 0.1%
2.4 1
 
< 0.1%
2.5 4
 
< 0.1%
2.6 1
 
< 0.1%
2.67 3
 
< 0.1%
ValueCountFrequency (%)
5 3479
25.2%
4.99 14
 
0.1%
4.98 51
 
0.4%
4.97 75
 
0.5%
4.96 91
 
0.7%
4.95 111
 
0.8%
4.94 154
 
1.1%
4.93 128
 
0.9%
4.92 174
 
1.3%
4.91 133
 
1.0%

review_scores_location
Real number (ℝ)

Distinct108
Distinct (%)0.9%
Missing1689
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean4.8263986
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:40.470315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.4
Q14.75
median4.9
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.25385234
Coefficient of variation (CV)0.052596637
Kurtosis29.868369
Mean4.8263986
Median Absolute Deviation (MAD)0.1
Skewness-3.7504684
Sum58524.91
Variance0.064441009
MonotonicityNot monotonic
2023-01-21T15:08:40.622368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 4740
34.3%
4.67 408
 
3.0%
4.5 402
 
2.9%
4.75 379
 
2.7%
4.8 329
 
2.4%
4.83 323
 
2.3%
4.88 322
 
2.3%
4.89 276
 
2.0%
4.86 267
 
1.9%
4.92 229
 
1.7%
Other values (98) 4451
32.2%
(Missing) 1689
 
12.2%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 3
 
< 0.1%
2.5 1
 
< 0.1%
2.6 1
 
< 0.1%
2.86 1
 
< 0.1%
3 26
0.2%
3.17 1
 
< 0.1%
3.25 2
 
< 0.1%
3.33 1
 
< 0.1%
3.5 12
0.1%
ValueCountFrequency (%)
5 4740
34.3%
4.99 23
 
0.2%
4.98 78
 
0.6%
4.97 123
 
0.9%
4.96 122
 
0.9%
4.95 148
 
1.1%
4.94 204
 
1.5%
4.93 182
 
1.3%
4.92 229
 
1.7%
4.91 181
 
1.3%

review_scores_accuracy
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct118
Distinct (%)1.0%
Missing1688
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean4.8453377
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:40.774923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.43
Q14.8
median4.93
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.26261914
Coefficient of variation (CV)0.054200378
Kurtosis49.131053
Mean4.8453377
Median Absolute Deviation (MAD)0.07
Skewness-5.0182151
Sum58759.41
Variance0.06896881
MonotonicityNot monotonic
2023-01-21T15:08:40.983247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 5073
36.7%
4.88 356
 
2.6%
4.67 351
 
2.5%
4.75 323
 
2.3%
4.86 321
 
2.3%
4.5 319
 
2.3%
4.83 315
 
2.3%
4.8 302
 
2.2%
4.92 273
 
2.0%
4.9 246
 
1.8%
Other values (108) 4248
30.7%
(Missing) 1688
 
12.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 7
 
0.1%
2 3
 
< 0.1%
2.5 4
 
< 0.1%
3 24
0.2%
3.25 2
 
< 0.1%
3.33 6
 
< 0.1%
3.38 1
 
< 0.1%
3.4 2
 
< 0.1%
3.41 1
 
< 0.1%
ValueCountFrequency (%)
5 5073
36.7%
4.99 16
 
0.1%
4.98 69
 
0.5%
4.97 126
 
0.9%
4.96 169
 
1.2%
4.95 214
 
1.5%
4.94 242
 
1.8%
4.93 243
 
1.8%
4.92 273
 
2.0%
4.91 241
 
1.7%

review_scores_communication
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct117
Distinct (%)1.0%
Missing1688
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean4.9051398
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:41.201246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.55
Q14.9
median5
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.2354877
Coefficient of variation (CV)0.048008357
Kurtosis81.809339
Mean4.9051398
Median Absolute Deviation (MAD)0
Skewness-7.0826237
Sum59484.63
Variance0.055454456
MonotonicityNot monotonic
2023-01-21T15:08:41.401850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 6926
50.1%
4.94 311
 
2.3%
4.88 273
 
2.0%
4.93 270
 
2.0%
4.96 263
 
1.9%
4.92 253
 
1.8%
4.97 245
 
1.8%
4.95 238
 
1.7%
4.89 229
 
1.7%
4.9 214
 
1.5%
Other values (107) 2905
21.0%
(Missing) 1688
 
12.2%
ValueCountFrequency (%)
1 9
0.1%
2 6
 
< 0.1%
2.25 1
 
< 0.1%
2.33 1
 
< 0.1%
2.5 1
 
< 0.1%
3 18
0.1%
3.17 1
 
< 0.1%
3.25 3
 
< 0.1%
3.33 2
 
< 0.1%
3.38 1
 
< 0.1%
ValueCountFrequency (%)
5 6926
50.1%
4.99 54
 
0.4%
4.98 172
 
1.2%
4.97 245
 
1.8%
4.96 263
 
1.9%
4.95 238
 
1.7%
4.94 311
 
2.3%
4.93 270
 
2.0%
4.92 253
 
1.8%
4.91 184
 
1.3%

review_scores_checkin
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct107
Distinct (%)0.9%
Missing1688
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean4.88394
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:41.595595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.5
Q14.86
median4.98
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.25234041
Coefficient of variation (CV)0.051667386
Kurtosis82.268207
Mean4.88394
Median Absolute Deviation (MAD)0.02
Skewness-7.0078609
Sum59227.54
Variance0.063675684
MonotonicityNot monotonic
2023-01-21T15:08:41.820202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 6023
43.6%
4.88 341
 
2.5%
4.94 303
 
2.2%
4.92 288
 
2.1%
4.86 271
 
2.0%
4.93 266
 
1.9%
4.9 260
 
1.9%
4.95 255
 
1.8%
4.75 247
 
1.8%
4.91 246
 
1.8%
Other values (97) 3627
26.3%
(Missing) 1688
 
12.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 11
0.1%
2 8
0.1%
2.67 3
 
< 0.1%
2.75 1
 
< 0.1%
3 18
0.1%
3.17 1
 
< 0.1%
3.33 1
 
< 0.1%
3.38 1
 
< 0.1%
3.4 1
 
< 0.1%
ValueCountFrequency (%)
5 6023
43.6%
4.99 27
 
0.2%
4.98 145
 
1.0%
4.97 199
 
1.4%
4.96 229
 
1.7%
4.95 255
 
1.8%
4.94 303
 
2.2%
4.93 266
 
1.9%
4.92 288
 
2.1%
4.91 246
 
1.8%

review_scores_rating
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct124
Distinct (%)1.0%
Missing1645
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean4.7871446
Minimum0
Maximum5
Zeros43
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:42.005161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.29
Q14.71
median4.89
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.29

Descriptive statistics

Standard deviation0.40075638
Coefficient of variation (CV)0.083715119
Kurtosis75.709681
Mean4.7871446
Median Absolute Deviation (MAD)0.11
Skewness-7.2082105
Sum58259.55
Variance0.16060568
MonotonicityNot monotonic
2023-01-21T15:08:42.142819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 4483
32.5%
4.67 460
 
3.3%
4.5 377
 
2.7%
4.75 353
 
2.6%
4.83 333
 
2.4%
4.8 327
 
2.4%
4.88 305
 
2.2%
4.86 291
 
2.1%
4 240
 
1.7%
4.92 229
 
1.7%
Other values (114) 4772
34.5%
(Missing) 1645
 
11.9%
ValueCountFrequency (%)
0 43
0.3%
1 5
 
< 0.1%
2 5
 
< 0.1%
2.5 2
 
< 0.1%
2.75 1
 
< 0.1%
2.8 1
 
< 0.1%
3 36
0.3%
3.2 1
 
< 0.1%
3.25 1
 
< 0.1%
3.29 1
 
< 0.1%
ValueCountFrequency (%)
5 4483
32.5%
4.99 7
 
0.1%
4.98 39
 
0.3%
4.97 86
 
0.6%
4.96 121
 
0.9%
4.95 130
 
0.9%
4.94 212
 
1.5%
4.93 179
 
1.3%
4.92 229
 
1.7%
4.91 212
 
1.5%

maximum_nights
Real number (ℝ)

Distinct144
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.61158
Minimum1
Maximum4000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size215.9 KiB
2023-01-21T15:08:42.319368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q118
median365
Q31125
95-th percentile1125
Maximum4000
Range3999
Interquartile range (IQR)1107

Descriptive statistics

Standard deviation499.80258
Coefficient of variation (CV)1.0575335
Kurtosis-1.3055101
Mean472.61158
Median Absolute Deviation (MAD)352
Skewness0.48923997
Sum6529129
Variance249802.62
MonotonicityNot monotonic
2023-01-21T15:08:42.464989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1125 4792
34.7%
365 2237
16.2%
14 1123
 
8.1%
30 837
 
6.1%
7 716
 
5.2%
21 458
 
3.3%
10 422
 
3.1%
20 266
 
1.9%
60 233
 
1.7%
28 182
 
1.3%
Other values (134) 2549
18.5%
ValueCountFrequency (%)
1 10
 
0.1%
2 29
 
0.2%
3 77
 
0.6%
4 109
 
0.8%
5 141
 
1.0%
6 143
 
1.0%
7 716
5.2%
8 154
 
1.1%
9 70
 
0.5%
10 422
3.1%
ValueCountFrequency (%)
4000 2
 
< 0.1%
1125 4792
34.7%
1124 50
 
0.4%
1123 1
 
< 0.1%
1122 2
 
< 0.1%
1120 14
 
0.1%
1111 1
 
< 0.1%
1100 2
 
< 0.1%
1095 1
 
< 0.1%
1000 13
 
0.1%

listing_url
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct13815
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size215.9 KiB
https://www.airbnb.com/rooms/6983
 
1
https://www.airbnb.com/rooms/52756315
 
1
https://www.airbnb.com/rooms/52769733
 
1
https://www.airbnb.com/rooms/52782453
 
1
https://www.airbnb.com/rooms/52782721
 
1
Other values (13810)
13810 

Length

Max length47
Median length37
Mean length39.507275
Min length33

Characters and Unicode

Total characters545793
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13815 ?
Unique (%)100.0%

Sample

1st rowhttps://www.airbnb.com/rooms/6983
2nd rowhttps://www.airbnb.com/rooms/26057
3rd rowhttps://www.airbnb.com/rooms/26473
4th rowhttps://www.airbnb.com/rooms/29118
5th rowhttps://www.airbnb.com/rooms/31094

Common Values

ValueCountFrequency (%)
https://www.airbnb.com/rooms/6983 1
 
< 0.1%
https://www.airbnb.com/rooms/52756315 1
 
< 0.1%
https://www.airbnb.com/rooms/52769733 1
 
< 0.1%
https://www.airbnb.com/rooms/52782453 1
 
< 0.1%
https://www.airbnb.com/rooms/52782721 1
 
< 0.1%
https://www.airbnb.com/rooms/52786282 1
 
< 0.1%
https://www.airbnb.com/rooms/52795544 1
 
< 0.1%
https://www.airbnb.com/rooms/52800828 1
 
< 0.1%
https://www.airbnb.com/rooms/52803374 1
 
< 0.1%
https://www.airbnb.com/rooms/52804784 1
 
< 0.1%
Other values (13805) 13805
99.9%

Length

2023-01-21T15:08:42.649760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.airbnb.com/rooms/6983 1
 
< 0.1%
https://www.airbnb.com/rooms/313237 1
 
< 0.1%
https://www.airbnb.com/rooms/26473 1
 
< 0.1%
https://www.airbnb.com/rooms/29118 1
 
< 0.1%
https://www.airbnb.com/rooms/31094 1
 
< 0.1%
https://www.airbnb.com/rooms/32379 1
 
< 0.1%
https://www.airbnb.com/rooms/32841 1
 
< 0.1%
https://www.airbnb.com/rooms/33680 1
 
< 0.1%
https://www.airbnb.com/rooms/37159 1
 
< 0.1%
https://www.airbnb.com/rooms/38499 1
 
< 0.1%
Other values (13805) 13805
99.9%

Most occurring characters

ValueCountFrequency (%)
/ 55260
 
10.1%
w 41445
 
7.6%
o 41445
 
7.6%
s 27630
 
5.1%
. 27630
 
5.1%
r 27630
 
5.1%
b 27630
 
5.1%
t 27630
 
5.1%
m 27630
 
5.1%
6 15993
 
2.9%
Other values (16) 225870
41.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 303930
55.7%
Decimal Number 145158
26.6%
Other Punctuation 96705
 
17.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 41445
13.6%
o 41445
13.6%
s 27630
9.1%
r 27630
9.1%
b 27630
9.1%
t 27630
9.1%
m 27630
9.1%
h 13815
 
4.5%
c 13815
 
4.5%
n 13815
 
4.5%
Other values (3) 41445
13.6%
Decimal Number
ValueCountFrequency (%)
6 15993
11.0%
5 15549
10.7%
3 15459
10.6%
1 15146
10.4%
4 15051
10.4%
2 14913
10.3%
7 13579
9.4%
0 13228
9.1%
9 13175
9.1%
8 13065
9.0%
Other Punctuation
ValueCountFrequency (%)
/ 55260
57.1%
. 27630
28.6%
: 13815
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 303930
55.7%
Common 241863
44.3%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 55260
22.8%
. 27630
11.4%
6 15993
 
6.6%
5 15549
 
6.4%
3 15459
 
6.4%
1 15146
 
6.3%
4 15051
 
6.2%
2 14913
 
6.2%
: 13815
 
5.7%
7 13579
 
5.6%
Other values (3) 39468
16.3%
Latin
ValueCountFrequency (%)
w 41445
13.6%
o 41445
13.6%
s 27630
9.1%
r 27630
9.1%
b 27630
9.1%
t 27630
9.1%
m 27630
9.1%
h 13815
 
4.5%
c 13815
 
4.5%
n 13815
 
4.5%
Other values (3) 41445
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 545793
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 55260
 
10.1%
w 41445
 
7.6%
o 41445
 
7.6%
s 27630
 
5.1%
. 27630
 
5.1%
r 27630
 
5.1%
b 27630
 
5.1%
t 27630
 
5.1%
m 27630
 
5.1%
6 15993
 
2.9%
Other values (16) 225870
41.4%
Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size134.9 KiB
False
12310 
True
1503 
(Missing)
 
2
ValueCountFrequency (%)
False 12310
89.1%
True 1503
 
10.9%
(Missing) 2
 
< 0.1%
2023-01-21T15:08:42.795247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

host_about
Categorical

HIGH CARDINALITY  MISSING 

Distinct5856
Distinct (%)85.6%
Missing6970
Missing (%)50.5%
Memory size215.9 KiB
Vi udlejer møblerede lejligheder og har mere end 10 års erfaring. Vores mission er at udleje fuldt møblerede lejligheder og hotellejligheder med de bedste beliggenheder i København til turister, forretningsrejsende, virksomheder og deres medarbejdere. Vi er til stede i København og står til rådighed 24/7 for vore gæster og ejere.
 
179
Sanders provides quality stays around the world with a hotel-like service done better.
 
55
Seneca Service Company has a variety of furnished services apartments located in Central Copenhagen.
 
26
Rent A Place, has apartments on the best locations primary in Inner City of Copenhagen. We are only located in the center of the center and sights, shopping or metro is just a small walk away. . Are you looking for prime locations, cosy interiors and attentive service? We’re here to give you a welcoming & local experience in Copenhagen. All of our apartments has fast wifi and complimentary in-room service. So if you are looking for a comfortable place that lets you do more than just sleep, we are here to help. Making you feel at home and otherwise staying out of your way, is our top priority. By preparing everything ahead of time, you and your travel companions will enjoy the privacy and flexibility during your visit. Check in to your many of our apartments at your own with your own passcode. ‍ If you need us during your stay, are we always a sms away. Rent A Place
 
24
.
 
23
Other values (5851)
6538 

Length

Max length6640
Median length788
Mean length261.37327
Min length1

Characters and Unicode

Total characters1789100
Distinct characters243
Distinct categories20 ?
Distinct scripts7 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5433 ?
Unique (%)79.4%

Sample

1st rowI'm currently working as an environmental consultant for a large engineering consultancy in Copenhagen. When I'm not at work, I spend time doing sports (playing football, running, cross fit), or doing indoor activities such as reading books and listening to music. I have recently taken an interest in cooking, and I love great food. I'm outgoing, happy and love good company. And I love my bike as any other person from Copenhagen..
2nd rowWe are a family with 2 children, and living in a great and beautiful house placed in a very special part in central Copenhagen called Kartoffelraekkerne. It's like a small village in the center of the city! The house is available from time to time - when we are travelling/working abroad. We're looking forward to hearing from you.
3rd rowActive young woman, started as an expat in Copenhagen and settled it as a new home. During this process, I have learnt a lot about the art of housing, and made it my daily activity. While I prefer to let you conquer the city on your own paths, please feel free to use my experience if you need any tips & advice. Will do my best to give you a perfect slice of Copenhagen. In my spare time I love consuming good food, taking long bike rides or discovering new music. I live & work here and can be contacted with any comments & questions.
4th rowI have a Master of Arts in Musicology and I work as a vocal coach and a singer and I LOVE to travel and meet people from all over the world. I live in lively Vesterbro with my 5 yr. old son Wili.
5th rowHi and welcome. My name is Ebbe, I am a medical doctor working in Copenhagen. I live in the flat with my girlfriend Lea who is working as a nurse. We have two little girls: Nora is 6 years old and Luna is 2 years old. We love sports, music and travelling, and we look forward welcoming you to Wonderful Copenhagen :-)

Common Values

ValueCountFrequency (%)
Vi udlejer møblerede lejligheder og har mere end 10 års erfaring. Vores mission er at udleje fuldt møblerede lejligheder og hotellejligheder med de bedste beliggenheder i København til turister, forretningsrejsende, virksomheder og deres medarbejdere. Vi er til stede i København og står til rådighed 24/7 for vore gæster og ejere. 179
 
1.3%
Sanders provides quality stays around the world with a hotel-like service done better. 55
 
0.4%
Seneca Service Company has a variety of furnished services apartments located in Central Copenhagen. 26
 
0.2%
Rent A Place, has apartments on the best locations primary in Inner City of Copenhagen. We are only located in the center of the center and sights, shopping or metro is just a small walk away. . Are you looking for prime locations, cosy interiors and attentive service? We’re here to give you a welcoming & local experience in Copenhagen. All of our apartments has fast wifi and complimentary in-room service. So if you are looking for a comfortable place that lets you do more than just sleep, we are here to help. Making you feel at home and otherwise staying out of your way, is our top priority. By preparing everything ahead of time, you and your travel companions will enjoy the privacy and flexibility during your visit. Check in to your many of our apartments at your own with your own passcode. ‍ If you need us during your stay, are we always a sms away. Rent A Place 24
 
0.2%
. 23
 
0.2%
We’re Blueground, a global proptech company with several thousand move-in-ready apartments in a growing number of major cities around the world. With flexible terms and homes in vibrant, centrally based neighborhoods, you’ll feel at home and free to roam for as long as you want — a month, a year, or longer. Each apartment is thoughtfully designed with exclusive furnishings, fully equipped kitchens, and incredible amenities – making every day a five-star experience. From day one, you’ll enjoy high-speed Wi-Fi, premium linens, and smart home entertainment. Plus, access to pools, gyms, and outdoor spaces in select buildings. Why stress over your apartment? We provide a hassle-free alternative — a consistent, quality guest experience that starts even before you arrive. Because we let you book our most up-to-date apartment listings online, confirm with a click, pay securely, and check in easily. During your stay Upon arrival, you’ll either be greeted personally by a Blueground team member or given self-check-in instructions. The entire apartment is yours! You’ll enjoy reliable support via email, phone, and our Guest App, where you can request everything from a home cleaning to extra towels. We’ll share all details upon confirmation of your stay. 16
 
0.1%
Are you looking for prime locations, Nordic interiors and attentive service? We’re here to give you a welcoming & truly local experience in Copenhagen. All of our apartments have designer bathrooms, fast wifi and complimentary in-room media entertainment. So if you are looking for a comfortable place that lets you do more than just sleep, we are here to help. Making you feel at home and otherwise staying out of your way is our top priority. By preparing everything ahead of time, you and your travel companions will enjoy the luxury of privacy and flexibility during your visit. Check in to your apartment at your own leisure, day and night, with your passcode. Enjoy a cup of coffee while you hop on the wifi and settle into your new place. Should you have any special requests like a scheduled taxi pick up or laundry service, just let us know. We'll be happy to arrange it for you. ‍ If you need us during your stay, our property managers are always just a call, sms or an email away. 13
 
0.1%
Located in the best neighbourhoods, Into This Place's unique and high end rentals offer the space and amenities of a hotel apartment combined with the quality and consistency of a boutique hotel. In all our living spaces our finest mission is to make you feel at home and local while away. The design of each living space is unique and our focus is on Scandinavian design combined with lots of danish “hygge” (coziness). By removing some of the services a traditional hotel offers as the 24/7 reception, lobby area, breakfast restaurant etc. and instead having our team anticipate needs and create a high level of behind-the-scenes-service we are able to provide quality living spaces with invisible service at affordable prices. All our listings are licensed and we live up to the high requirements for hotels within fire safety which most other AIRBNB listings unfortunately do not. 13
 
0.1%
Are you looking for prime locations, Nordic interiors and attentive service? We’re here to give you a welcoming & truly local experience in Copenhagen. All of our apartments have designer bathrooms, fast wifi and complimentary in-room media entertainment. So if you are looking for a comfortable place that lets you do more than just sleep, we are here to help. Making you feel at home and otherwise staying out of your way is our top priority. By preparing everything ahead of time, you and your travel companions will enjoy the luxury of privacy and flexibility during your visit. Check in to your apartment at your own leisure, day and night, with your passcode. Enjoy a cup of coffee while you hop on the wifi and settle into your new place. Should you have any special requests like a scheduled taxi pick up or laundry service, just let us know. We'll be happy to arrange it for you. ‍ If you need us during your stay, our property managers are always just a call, sms or an email away. 12
 
0.1%
... 11
 
0.1%
Other values (5846) 6473
46.9%
(Missing) 6970
50.5%

Length

2023-01-21T15:08:42.923856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 14729
 
4.6%
i 10684
 
3.3%
in 8093
 
2.5%
a 7976
 
2.5%
to 7885
 
2.5%
the 7354
 
2.3%
my 4943
 
1.5%
of 4411
 
1.4%
we 4324
 
1.3%
copenhagen 3906
 
1.2%
Other values (14278) 246780
76.9%

Most occurring characters

ValueCountFrequency (%)
314251
17.6%
e 176561
 
9.9%
a 111866
 
6.3%
n 106796
 
6.0%
o 103812
 
5.8%
t 94436
 
5.3%
i 94267
 
5.3%
r 92469
 
5.2%
s 68794
 
3.8%
l 65472
 
3.7%
Other values (233) 560376
31.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1343117
75.1%
Space Separator 314267
 
17.6%
Uppercase Letter 51635
 
2.9%
Other Punctuation 43761
 
2.4%
Control 17414
 
1.0%
Decimal Number 10903
 
0.6%
Dash Punctuation 3313
 
0.2%
Close Punctuation 2133
 
0.1%
Open Punctuation 1120
 
0.1%
Final Punctuation 901
 
0.1%
Other values (10) 536
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7
 
4.4%
6
 
3.8%
6
 
3.8%
6
 
3.8%
5
 
3.1%
4
 
2.5%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
Other values (80) 113
71.1%
Lowercase Letter
ValueCountFrequency (%)
e 176561
13.1%
a 111866
 
8.3%
n 106796
 
8.0%
o 103812
 
7.7%
t 94436
 
7.0%
i 94267
 
7.0%
r 92469
 
6.9%
s 68794
 
5.1%
l 65472
 
4.9%
d 53267
 
4.0%
Other values (39) 375377
27.9%
Uppercase Letter
ValueCountFrequency (%)
I 12209
23.6%
C 5485
 
10.6%
W 3820
 
7.4%
A 3394
 
6.6%
M 2544
 
4.9%
S 2389
 
4.6%
D 2331
 
4.5%
H 2259
 
4.4%
V 1767
 
3.4%
T 1670
 
3.2%
Other values (22) 13767
26.7%
Other Punctuation
ValueCountFrequency (%)
. 20324
46.4%
, 16102
36.8%
' 2489
 
5.7%
! 1578
 
3.6%
: 1449
 
3.3%
/ 697
 
1.6%
& 372
 
0.9%
" 317
 
0.7%
; 203
 
0.5%
? 151
 
0.3%
Other values (7) 79
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 2099
19.3%
1 1962
18.0%
0 1604
14.7%
3 1309
12.0%
4 1059
9.7%
5 925
8.5%
7 585
 
5.4%
9 509
 
4.7%
6 431
 
4.0%
8 420
 
3.9%
Control
ValueCountFrequency (%)
12120
69.6%
5272
30.3%
€ 8
 
< 0.1%
6
 
< 0.1%
œ 3
 
< 0.1%
 3
 
< 0.1%
™ 2
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 50
61.0%
= 12
 
14.6%
< 10
 
12.2%
| 5
 
6.1%
~ 4
 
4.9%
× 1
 
1.2%
Other Symbol
ValueCountFrequency (%)
6
31.6%
5
26.3%
4
21.1%
2
 
10.5%
® 1
 
5.3%
¦ 1
 
5.3%
Modifier Symbol
ValueCountFrequency (%)
´ 123
92.5%
` 4
 
3.0%
^ 4
 
3.0%
¨ 2
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 3217
97.1%
60
 
1.8%
36
 
1.1%
Close Punctuation
ValueCountFrequency (%)
) 2130
99.9%
] 2
 
0.1%
} 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 1117
99.7%
[ 2
 
0.2%
{ 1
 
0.1%
Modifier Letter
ValueCountFrequency (%)
3
33.3%
3
33.3%
3
33.3%
Space Separator
ValueCountFrequency (%)
314251
> 99.9%
  16
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
846
93.9%
55
 
6.1%
Initial Punctuation
ValueCountFrequency (%)
50
87.7%
7
 
12.3%
Format
ValueCountFrequency (%)
49
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 15
100.0%
Nonspacing Mark
ValueCountFrequency (%)
11
100.0%
Other Number
ValueCountFrequency (%)
½ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1394752
78.0%
Common 394129
 
22.0%
Hiragana 76
 
< 0.1%
Inherited 60
 
< 0.1%
Han 50
 
< 0.1%
Katakana 18
 
< 0.1%
Hangul 15
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 176561
12.7%
a 111866
 
8.0%
n 106796
 
7.7%
o 103812
 
7.4%
t 94436
 
6.8%
i 94267
 
6.8%
r 92469
 
6.6%
s 68794
 
4.9%
l 65472
 
4.7%
d 53267
 
3.8%
Other values (71) 427012
30.6%
Common
ValueCountFrequency (%)
314251
79.7%
. 20324
 
5.2%
, 16102
 
4.1%
12120
 
3.1%
5272
 
1.3%
- 3217
 
0.8%
' 2489
 
0.6%
) 2130
 
0.5%
2 2099
 
0.5%
1 1962
 
0.5%
Other values (60) 14163
 
3.6%
Han
ValueCountFrequency (%)
3
 
6.0%
3
 
6.0%
3
 
6.0%
2
 
4.0%
2
 
4.0%
2
 
4.0%
2
 
4.0%
2
 
4.0%
1
 
2.0%
1
 
2.0%
Other values (29) 29
58.0%
Hiragana
ValueCountFrequency (%)
7
 
9.2%
6
 
7.9%
6
 
7.9%
6
 
7.9%
5
 
6.6%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
Other values (20) 31
40.8%
Hangul
ValueCountFrequency (%)
2
13.3%
2
13.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other values (3) 3
20.0%
Katakana
ValueCountFrequency (%)
4
22.2%
3
16.7%
3
16.7%
2
11.1%
2
11.1%
2
11.1%
1
 
5.6%
1
 
5.6%
Inherited
ValueCountFrequency (%)
49
81.7%
11
 
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1779975
99.5%
None 7829
 
0.4%
Punctuation 1127
 
0.1%
Hiragana 76
 
< 0.1%
CJK 50
 
< 0.1%
Hangul 15
 
< 0.1%
Dingbats 12
 
< 0.1%
VS 11
 
< 0.1%
Misc Symbols 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
314251
17.7%
e 176561
 
9.9%
a 111866
 
6.3%
n 106796
 
6.0%
o 103812
 
5.8%
t 94436
 
5.3%
i 94267
 
5.3%
r 92469
 
5.2%
s 68794
 
3.9%
l 65472
 
3.7%
Other values (85) 551251
31.0%
None
ValueCountFrequency (%)
å 3015
38.5%
ø 2735
34.9%
æ 1555
19.9%
´ 123
 
1.6%
é 118
 
1.5%
Ø 107
 
1.4%
  16
 
0.2%
á 13
 
0.2%
ü 11
 
0.1%
ñ 11
 
0.1%
Other values (42) 125
 
1.6%
Punctuation
ValueCountFrequency (%)
846
75.1%
60
 
5.3%
55
 
4.9%
50
 
4.4%
49
 
4.3%
36
 
3.2%
15
 
1.3%
9
 
0.8%
7
 
0.6%
VS
ValueCountFrequency (%)
11
100.0%
Hiragana
ValueCountFrequency (%)
7
 
9.2%
6
 
7.9%
6
 
7.9%
6
 
7.9%
5
 
6.6%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
Other values (20) 31
40.8%
Dingbats
ValueCountFrequency (%)
6
50.0%
4
33.3%
2
 
16.7%
Misc Symbols
ValueCountFrequency (%)
5
100.0%
CJK
ValueCountFrequency (%)
3
 
6.0%
3
 
6.0%
3
 
6.0%
2
 
4.0%
2
 
4.0%
2
 
4.0%
2
 
4.0%
2
 
4.0%
1
 
2.0%
1
 
2.0%
Other values (29) 29
58.0%
Hangul
ValueCountFrequency (%)
2
13.3%
2
13.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other values (3) 3
20.0%

host_response_time
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing3753
Missing (%)27.2%
Memory size215.9 KiB
within an hour
4835 
within a day
2469 
within a few hours
2281 
a few days or more
 
477

Length

Max length18
Median length14
Mean length14.605645
Min length12

Characters and Unicode

Total characters146962
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwithin a few hours
2nd rowwithin a few hours
3rd rowwithin an hour
4th rowwithin a few hours
5th rowwithin a day

Common Values

ValueCountFrequency (%)
within an hour 4835
35.0%
within a day 2469
17.9%
within a few hours 2281
16.5%
a few days or more 477
 
3.5%
(Missing) 3753
27.2%

Length

2023-01-21T15:08:43.092941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T15:08:43.293139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
within 9585
28.7%
a 5227
15.6%
an 4835
14.5%
hour 4835
14.5%
few 2758
 
8.3%
day 2469
 
7.4%
hours 2281
 
6.8%
days 477
 
1.4%
or 477
 
1.4%
more 477
 
1.4%

Most occurring characters

ValueCountFrequency (%)
23359
15.9%
i 19170
13.0%
h 16701
11.4%
n 14420
9.8%
a 13008
8.9%
w 12343
8.4%
t 9585
6.5%
o 8070
 
5.5%
r 8070
 
5.5%
u 7116
 
4.8%
Other values (6) 15120
10.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 123603
84.1%
Space Separator 23359
 
15.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 19170
15.5%
h 16701
13.5%
n 14420
11.7%
a 13008
10.5%
w 12343
10.0%
t 9585
7.8%
o 8070
6.5%
r 8070
6.5%
u 7116
 
5.8%
e 3235
 
2.6%
Other values (5) 11885
9.6%
Space Separator
ValueCountFrequency (%)
23359
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 123603
84.1%
Common 23359
 
15.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 19170
15.5%
h 16701
13.5%
n 14420
11.7%
a 13008
10.5%
w 12343
10.0%
t 9585
7.8%
o 8070
6.5%
r 8070
6.5%
u 7116
 
5.8%
e 3235
 
2.6%
Other values (5) 11885
9.6%
Common
ValueCountFrequency (%)
23359
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 146962
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23359
15.9%
i 19170
13.0%
h 16701
11.4%
n 14420
9.8%
a 13008
8.9%
w 12343
8.4%
t 9585
6.5%
o 8070
 
5.5%
r 8070
 
5.5%
u 7116
 
4.8%
Other values (6) 15120
10.3%

host_response_rate
Categorical

HIGH CARDINALITY  HIGH CORRELATION  IMBALANCE  MISSING 

Distinct63
Distinct (%)0.6%
Missing3753
Missing (%)27.2%
Memory size215.9 KiB
100%
7664 
90%
 
315
0%
 
260
80%
 
219
50%
 
209
Other values (58)
1395 

Length

Max length4
Median length4
Mean length3.7358378
Min length2

Characters and Unicode

Total characters37590
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.1%

Sample

1st row100%
2nd row100%
3rd row100%
4th row100%
5th row100%

Common Values

ValueCountFrequency (%)
100% 7664
55.5%
90% 315
 
2.3%
0% 260
 
1.9%
80% 219
 
1.6%
50% 209
 
1.5%
99% 195
 
1.4%
60% 161
 
1.2%
75% 125
 
0.9%
67% 117
 
0.8%
70% 79
 
0.6%
Other values (53) 718
 
5.2%
(Missing) 3753
27.2%

Length

2023-01-21T15:08:43.397534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100 7664
76.2%
90 315
 
3.1%
0 260
 
2.6%
80 219
 
2.2%
50 209
 
2.1%
99 195
 
1.9%
60 161
 
1.6%
75 125
 
1.2%
67 117
 
1.2%
70 79
 
0.8%
Other values (53) 718
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0 16665
44.3%
% 10062
26.8%
1 7737
20.6%
9 910
 
2.4%
8 540
 
1.4%
7 430
 
1.1%
5 391
 
1.0%
6 383
 
1.0%
3 230
 
0.6%
2 142
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27528
73.2%
Other Punctuation 10062
 
26.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16665
60.5%
1 7737
28.1%
9 910
 
3.3%
8 540
 
2.0%
7 430
 
1.6%
5 391
 
1.4%
6 383
 
1.4%
3 230
 
0.8%
2 142
 
0.5%
4 100
 
0.4%
Other Punctuation
ValueCountFrequency (%)
% 10062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37590
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16665
44.3%
% 10062
26.8%
1 7737
20.6%
9 910
 
2.4%
8 540
 
1.4%
7 430
 
1.1%
5 391
 
1.0%
6 383
 
1.0%
3 230
 
0.6%
2 142
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37590
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16665
44.3%
% 10062
26.8%
1 7737
20.6%
9 910
 
2.4%
8 540
 
1.4%
7 430
 
1.1%
5 391
 
1.0%
6 383
 
1.0%
3 230
 
0.6%
2 142
 
0.4%

Interactions

2023-01-21T15:08:28.559009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:32.783216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:36.097929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:39.060403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:41.784132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:44.543188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:47.320951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:49.947614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:53.415228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:56.558818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:59.682545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:02.712056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:06.105341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:09.541936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:14.036465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:17.151776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:19.799829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:22.941084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:25.806582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:28.719131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:32.953009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:36.218893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:39.205005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-21T15:08:18.640911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:21.700837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:24.672244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:27.465893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:31.077119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:34.829837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:38.114578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:40.835877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:43.601563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:46.235323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:48.980443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:52.380263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:55.431434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:58.409884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:01.506051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:04.716341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:08.317934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:12.873839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:16.018232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:18.785247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:21.836242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:24.824683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:27.578319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:31.218358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:34.984628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:38.260212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:40.999764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:43.721258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:46.406598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:49.101500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:52.508701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:55.559865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:58.571600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:01.626716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:04.894880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:08.454220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:13.020554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:16.130566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:18.889701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:22.038127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:24.953036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:27.707940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:31.386554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:35.095590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:38.388475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:41.186898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:43.875402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:46.568280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:49.294257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:52.644841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:55.744202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:58.780670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:01.764876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:05.100071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:08.595052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:13.189590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:16.349530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:19.066350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:22.183520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:25.121510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:27.884125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:31.523325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:35.216924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:38.517151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:41.300588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:43.995977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:46.768106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:49.406797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:52.773486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:55.937480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:58.909773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:01.893139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:05.259486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:08.742559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:13.304285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:16.493620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:19.187263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:22.304945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:25.250832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:28.013691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:31.684470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:35.347637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:38.670948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:41.420907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:44.133224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:46.898426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:49.543468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:52.950615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:56.098873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:59.086264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:02.101730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:05.493903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:08.955906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:13.463403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:16.718527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:19.356336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:22.441423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:25.387503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:28.173470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:31.852890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:35.477969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:38.793124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:41.525536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:44.278166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:47.020447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:49.689471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:53.070680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:56.261420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:59.303472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:02.294608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:05.657290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:09.152668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:13.698029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:16.838047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:19.501250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:22.610670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:25.509263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:28.294156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:32.021836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:35.608414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:38.922937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:41.662992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:44.398362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:47.175421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:49.817952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:53.246466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:56.398249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:07:59.481073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:02.422708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:05.853401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:09.344268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:13.867007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:16.982459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:19.630687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:22.756286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:25.636962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T15:08:28.414556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-21T15:08:43.549888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
host_idlatitudelongitudepriceminimum_nightsnumber_of_reviewsreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmaccommodatesreview_scores_valuereview_scores_cleanlinessreview_scores_locationreview_scores_accuracyreview_scores_communicationreview_scores_checkinreview_scores_ratingmaximum_nightsneighbourhoodroom_typeproperty_typehost_is_superhosthost_response_timehost_response_rate
host_id1.000-0.0090.014-0.028-0.177-0.2040.2130.1000.0660.029-0.096-0.042-0.0250.006-0.005-0.021-0.012-0.033-0.0350.0360.0400.1490.1240.0720.201
latitude-0.0091.000-0.216-0.0610.040-0.016-0.034-0.0710.018-0.025-0.058-0.022-0.025-0.041-0.013-0.0000.007-0.010-0.0220.4780.0570.1530.0660.0460.070
longitude0.014-0.2161.0000.181-0.0260.0770.0460.0680.0370.0700.013-0.044-0.0190.129-0.022-0.043-0.037-0.0260.0290.5540.0730.1830.0470.0370.057
price-0.028-0.0610.1811.0000.013-0.103-0.0060.0170.209-0.0010.5220.0140.0760.2340.0760.0800.0830.1160.0460.0240.0000.0000.0170.0420.250
minimum_nights-0.1770.040-0.0260.0131.000-0.064-0.298-0.175-0.054-0.1840.1010.0970.0390.0470.0780.1140.1000.0950.0040.0000.0000.0000.0000.0030.000
number_of_reviews-0.204-0.0160.077-0.103-0.0641.0000.2410.011-0.0350.604-0.001-0.239-0.202-0.304-0.341-0.406-0.368-0.3170.0960.0340.1300.1390.2340.0590.051
reviews_per_month0.213-0.0340.046-0.006-0.2980.2411.0000.0610.2790.645-0.117-0.060-0.041-0.039-0.078-0.141-0.121-0.052-0.1020.0360.1640.2350.0840.0990.000
calculated_host_listings_count0.100-0.0710.0680.017-0.1750.0110.0611.0000.0970.031-0.002-0.156-0.074-0.041-0.139-0.172-0.114-0.1550.1560.0900.0300.3930.3300.1150.728
availability_3650.0660.0180.0370.209-0.054-0.0350.2790.0971.0000.142-0.016-0.101-0.0440.004-0.082-0.104-0.043-0.072-0.0240.0470.0650.0900.0210.0970.111
number_of_reviews_ltm0.029-0.0250.070-0.001-0.1840.6040.6450.0310.1421.000-0.032-0.128-0.119-0.093-0.172-0.225-0.215-0.130-0.0030.0390.1720.2440.1570.0520.028
accommodates-0.096-0.0580.0130.5220.101-0.001-0.117-0.002-0.016-0.0321.0000.002-0.0320.019-0.0170.0200.0340.0040.0950.0690.2840.2300.0430.0180.080
review_scores_value-0.042-0.022-0.0440.0140.097-0.239-0.060-0.156-0.101-0.1280.0021.0000.4980.3700.5510.4350.4190.643-0.0750.0270.0250.0670.0850.0370.086
review_scores_cleanliness-0.025-0.025-0.0190.0760.039-0.202-0.041-0.074-0.044-0.119-0.0320.4981.0000.2640.5310.3770.3680.613-0.0670.0240.0010.0070.1010.0410.117
review_scores_location0.006-0.0410.1290.2340.047-0.304-0.039-0.0410.004-0.0930.0190.3700.2641.0000.3360.3290.2920.383-0.0320.1070.0400.0720.0510.0240.096
review_scores_accuracy-0.005-0.013-0.0220.0760.078-0.341-0.078-0.139-0.082-0.172-0.0170.5510.5310.3361.0000.5300.5040.651-0.0780.0160.0850.1050.0630.0480.092
review_scores_communication-0.021-0.000-0.0430.0800.114-0.406-0.141-0.172-0.104-0.2250.0200.4350.3770.3290.5301.0000.5690.536-0.0800.0250.0400.0750.0470.0470.150
review_scores_checkin-0.0120.007-0.0370.0830.100-0.368-0.121-0.114-0.043-0.2150.0340.4190.3680.2920.5040.5691.0000.477-0.0790.0050.0470.0800.0480.0240.138
review_scores_rating-0.033-0.010-0.0260.1160.095-0.317-0.052-0.155-0.072-0.1300.0040.6430.6130.3830.6510.5360.4771.000-0.0800.0240.0410.0590.0780.0590.117
maximum_nights-0.035-0.0220.0290.0460.0040.096-0.1020.156-0.024-0.0030.095-0.075-0.067-0.032-0.078-0.080-0.079-0.0801.0000.0350.0130.1440.1060.0340.105
neighbourhood0.0360.4780.5540.0240.0000.0340.0360.0900.0470.0390.0690.0270.0240.1070.0160.0250.0050.0240.0351.0000.0750.1640.0830.0610.072
room_type0.0400.0570.0730.0000.0000.1300.1640.0300.0650.1720.2840.0250.0010.0400.0850.0400.0470.0410.0130.0751.0000.9610.1050.0520.199
property_type0.1490.1530.1830.0000.0000.1390.2350.3930.0900.2440.2300.0670.0070.0720.1050.0750.0800.0590.1440.1640.9611.0000.2900.1330.114
host_is_superhost0.1240.0660.0470.0170.0000.2340.0840.3300.0210.1570.0430.0850.1010.0510.0630.0470.0480.0780.1060.0830.1050.2901.0000.1720.350
host_response_time0.0720.0460.0370.0420.0030.0590.0990.1150.0970.0520.0180.0370.0410.0240.0480.0470.0240.0590.0340.0610.0520.1330.1721.0000.629
host_response_rate0.2010.0700.0570.2500.0000.0510.0000.7280.1110.0280.0800.0860.1170.0960.0920.1500.1380.1170.1050.0720.1990.1140.3500.6291.000

Missing values

2023-01-21T15:08:32.417909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-21T15:08:33.156786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-21T15:08:33.768389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

namehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlicenseproperty_typeaccommodatesfirst_reviewreview_scores_valuereview_scores_cleanlinessreview_scores_locationreview_scores_accuracyreview_scores_communicationreview_scores_checkinreview_scores_ratingmaximum_nightslisting_urlhost_is_superhosthost_abouthost_response_timehost_response_rate
id
6983Copenhagen 'N Livin'16774SimonNaNNrrebro55.68641012.547410Entire home/apt89831722022-06-211.08104NaNEntire rental unit22009-09-044.714.784.734.794.894.864.7815https://www.airbnb.com/rooms/6983fI'm currently working as an environmental consultant for a large engineering consultancy in Copenhagen.\r\nWhen I'm not at work, I spend time doing sports (playing football, running, cross fit), or doing indoor activities such as reading books and listening to music. I have recently taken an interest in cooking, and I love great food. \r\nI'm outgoing, happy and love good company.\r\nAnd I love my bike as any other person from Copenhagen..within a few hours100%
26057Lovely house - most attractive area109777KariNaNIndre By55.69307012.576490Entire home/apt26004592022-08-090.5513038NaNEntire home62013-12-024.814.964.944.934.864.934.911125https://www.airbnb.com/rooms/26057fWe are a family with 2 children, and living in a great and beautiful house placed in a very special part in central Copenhagen called Kartoffelraekkerne. It's like a small village in the center of the city! The house is available from time to time - when we are travelling/working abroad. We're looking forward to hearing from you.within a few hours100%
26473City Centre Townhouse Sleeps 1-10 persons112210JuliaNaNIndre By55.67602012.575400Entire home/apt325033002022-09-102.063567NaNEntire townhouse112010-10-144.594.444.894.634.704.784.5331https://www.airbnb.com/rooms/26473fActive young woman, started as an expat in Copenhagen and settled it as a new home. During this process, I have learnt a lot about the art of housing, and made it my daily activity. While I prefer to let you conquer the city on your own paths, please feel free to use my experience if you need any tips & advice.\n\nWill do my best to give you a perfect slice of Copenhagen.\n\nIn my spare time I love consuming good food, taking long bike rides or discovering new music.\n\nI live & work here and can be contacted with any comments & questions.within an hour100%
29118Best Location in Cool Istedgade125230NanaNaNVesterbro-Kongens Enghave55.67023012.555040Entire home/apt7257242022-08-040.161592NaNEntire rental unit22010-06-174.804.734.874.875.005.004.9214https://www.airbnb.com/rooms/29118fI have a Master of Arts in Musicology and I work as a vocal coach and a singer and I LOVE to travel and meet people from all over the world. \nI live in lively Vesterbro with my 5 yr. old son Wili.within a few hours100%
31094Beautiful, spacious, central, renovated Penthouse129976EbbeNaNVesterbro-Kongens Enghave55.66660212.555283Entire home/apt19543192022-08-220.13102NaNEntire condo62010-08-164.534.884.804.824.824.874.8810https://www.airbnb.com/rooms/31094fHi and welcome. My name is Ebbe, I am a medical doctor working in Copenhagen. I live in the flat with my girlfriend Lea who is working as a nurse. We have two little girls: Nora is 6 years old and Luna is 2 years old.\n\nWe love sports, music and travelling, and we look forward welcoming you to Wonderful Copenhagen :-)within a day100%
32379165 m2 artist flat on Vesterbro, with 2 bathrooms140105LiseNaNVesterbro-Kongens Enghave55.67263812.552493Entire home/apt12803802022-08-130.542605NaNEntire rental unit42010-08-234.724.924.884.964.914.884.90365https://www.airbnb.com/rooms/32379tAs profession - Set and Costumedesigner for Avangarde Theatre, Modern Dance and Performance. A traveler, enjoy to se the whole world, meet people and talk about the world situationwithin a few hours100%
32841Cozy flat for Adults/Quiet for kids142143Anders & MariaNaNsterbro55.71176012.570910Entire home/apt61710072016-09-150.0512810NaNEntire rental unit42010-07-254.504.504.504.755.005.004.571125https://www.airbnb.com/rooms/32841fAnders:\r\nHitchhiked 100.000 km's, Been publicly speaking more than 500 times, Traveled 80 countries, Lived 36 yrs. Have 7 sisters and brothers, Twins, 1 wife... and a million plans!\r\nMaria:\r\nSinger/nurse/Twin-Mum/training to be a midwife. Lived 6 years in London and toured the world with a British Pop band…including an appearance at the Bollywood Awards:-)NaNNaN
33680Best location on Vesterbro/Cph145671MetteNaNVesterbro-Kongens Enghave55.66631012.545550Entire home/apt10006712019-03-140.4813120NaNEntire rental unit42010-07-264.674.674.804.754.914.914.7460https://www.airbnb.com/rooms/33680fDanish Artist living in Copenhagen. I travel a lot and enjoy using airbnb both for my own flat but also to rent places. My area is great and I always enjoy to be here when Im home. I will be very happy to advise you on where to go and what to do when you are here. \r\nNaNNaN
37159Unique space on greatest location160390JeanetteNaNIndre By55.68547012.565430Entire home/apt29165112017-08-220.07100NaNEntire rental unit42010-08-114.785.004.894.785.005.005.0021https://www.airbnb.com/rooms/37159fHi my name is Jeanette and I live and work with my husband Casper in Copenhagen with something called Behavioral Architecture. Together we have 2 kids Elvis on 16 and Vivelill is 12. When we travel we love to go as locale as possible, to experience the nuances in the different cultures we visit :)NaNNaN
384990 min. from everything in Cph.122489ChristinaNaNIndre By55.68428812.573019Entire home/apt190014152022-09-170.101876NaNEntire rental unit52010-08-214.894.895.004.565.005.005.0070https://www.airbnb.com/rooms/38499fWe are...\nCarsten and Christina, and our awesome 3 kids. Carsten is a freelance photographer and Christina works in the NGO sector concerning refugees globally. Traveling is a passion we share, and airbnb has added greatly to the fantastic experiences we have had from the Vancouver skyline flat to the riad in Old Marrakech. \nWe love our city and enjoy going out or just hanging out people watching over the rim of a well brewed cup of latte in the parks around our place. \nYou are...\n...welcome to stay in our home whoever you are. As long as you take good care of it. We love our place, and we hope you will too.NaNNaN
namehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlicenseproperty_typeaccommodatesfirst_reviewreview_scores_valuereview_scores_cleanlinessreview_scores_locationreview_scores_accuracyreview_scores_communicationreview_scores_checkinreview_scores_ratingmaximum_nightslisting_urlhost_is_superhosthost_abouthost_response_timehost_response_rate
id
638839422729041798T31B, 1. A 1 bedroom apartment in Rødovre424093643SenecaNaNVanlse55.67286012.456100Entire home/apt490462022-08-212.172606NaNEntire serviced apartment22022-07-053.834.003.674.174.174.334.17365https://www.airbnb.com/rooms/638839422729041798fSeneca Service Company has a variety of furnished services apartments located in Central Copenhagen.within an hour100%
630779770351807878Skøn lejlighed i Hvidovre60240725HenrikNaNValby55.63870012.498240Entire home/apt100060NaNNaN100NaNEntire condo3NaNNaNNaNNaNNaNNaNNaNNaN1125https://www.airbnb.com/rooms/630779770351807878fNaNNaNNaN
6324741415496087863 Room apartment. 8 min walk from CPH Airport76731355JørgenNaNAmager st55.63090012.642480Entire home/apt845182022-09-112.11108NaNEntire condo42022-06-044.884.754.885.005.005.004.881125https://www.airbnb.com/rooms/632474141549608786fNaNwithin an hour100%
646726550705810749Ny rummelig lejlighed med tilhørende kat.54229471MortenNaNBrnshj-Husum55.73902812.487433Entire home/apt856642022-08-281.85104NaNEntire rental unit52022-07-235.004.004.504.755.005.004.757https://www.airbnb.com/rooms/646726550705810749fNaNwithin an hour100%
646941499450912133Big beautiful and charming apartment141288846TanjaNaNBispebjerg55.73048112.521243Entire home/apt1050242022-09-041.64204NaNEntire rental unit32022-07-155.005.004.754.505.005.005.0030https://www.airbnb.com/rooms/646941499450912133fNaNwithin an hour100%
653494030951422457Top-floor Villa Apartment in the Heart of Hellerup465189427MartineNaNsterbro55.73284012.572370Entire home/apt1250140NaNNaN100NaNEntire condo4NaNNaNNaNNaNNaNNaNNaNNaN50https://www.airbnb.com/rooms/653494030951422457fNaNNaNNaN
647809303952891559Dejligt lille hus med flere hyggekroge ude og inde4862421KathrineNaNValby55.66778212.463285Entire home/apt450332022-07-211.01103NaNEntire home42022-06-295.005.004.675.005.005.005.001125https://www.airbnb.com/rooms/647809303952891559fI´m 39, living in Rødovre, Copenhagen with my son who is 6 years old. I work with recruiting and spend most of my free time with my son enjoying time with neighbours and friends in the garden or the nice area with the big lake, Damhussøen. I also love spending time inside being cozy with a cup of tea and cooking to my friends and family.within a day100%
650819220455514228Lejlighed i Storkøbenhavn. 13 minutter fra centrum134647873TimNaNValby55.65953612.474726Entire home/apt400132022-08-141.27103NaNEntire condo32022-07-175.004.674.675.005.005.005.00365https://www.airbnb.com/rooms/650819220455514228fNaNwithin an hour100%
658612163688161695Cosy apartment with a great view in Copenhagen256903668SamNaNBrnshj-Husum55.73097112.487993Entire home/apt85010NaNNaN100NaNEntire rental unit2NaNNaNNaNNaNNaNNaNNaNNaN365https://www.airbnb.com/rooms/658612163688161695fNaNwithin a few hours100%
648436253362373119Big Bedroom connected with a large living room141288846TanjaNaNBispebjerg55.73142012.521770Private room420212022-07-070.37201NaNPrivate room in rental unit22022-07-075.005.005.005.005.005.005.0030https://www.airbnb.com/rooms/648436253362373119fNaNwithin an hour100%